Forecasting
All Publication Topics
Autonomous Driving
Autonomous Exploration
Benchmarks
Compression
Computer Vision
First-Person Video
Forecasting
Generative Models
Imitation Learning
Intrinsic Control
Machine Learning
Manipulation
Model-Based Control
Offroad Navigation
Paper Award
Reinforcement Learning
Reward Learning
Robotics
Social Navigation
OSCAR: Obstacle Survival Curves for Adaptive Robot Navigation.
arXiv:2606.00990, (arXiv), 2026.
arXiv:2606.00990, (arXiv), 2026.
A mobile robot following a graph of known routes can make costly navigation errors when a temporary obstacle blocks a critical edge: waiting too long behind a parked cart wastes time, but immediately rerouting around a person who would move in a few seconds is also inefficient. Standard reactive obstacle avoidance addresses local motion around obstacles, while fixed wait-or-reroute rules ignore how long different obstacle types tend to persist. We propose OSCAR: an adaptive survival-modeling framework for graph-based navigation with temporary blockages. Assuming obstacle class labels are available at encounter time, the robot learns class-conditioned residual clearance-time distributions from online experience, including right-censored observations when it reroutes before observing clearance. These survival models are integrated into a time-dependent graph planner that maintains obstacle memory and computes a patience threshold at each blocked edge: how long to wait before taking an alternate route. The method continuously updates its clearance estimates across episodes and uses them to balance waiting against rerouting. We evaluate the approach in simulation and on a real mobile robot in a university atrium with obstacles including people, chairs, bins, and tubes. In simulation, the learned policy’s time-to-goal converges to within 1% of an oracle with access to ground-truth clearance distributions after fewer than 20 observations per obstacle class, outperforming all heuristic baselines. Real-world deployment confirms that the policy improves online, adapting its patience thresholds from experience across 50 navigation episodes.
@misc{sahak2026oscar,
archiveprefix = {arXiv},
author = {Hshmat Sahak and Aoran Jiao and Nicholas Rhinehart and Tim Barfoot},
eprint = {2606.00990},
primaryclass = {cs.RO},
title = {OSCAR: Obstacle Survival Curves for Adaptive Robot Navigation},
url = {https://arxiv.org/abs/2606.00990},
year = {2026}
}
OccSim: Multi-kilometer Simulation with Long-horizon Occupancy World Models.
arXiv:2603.28887, (arXiv), 2026.
arXiv:2603.28887, (arXiv), 2026.
Data-driven autonomous driving simulation has long been constrained by its heavy reliance on pre-recorded driving logs or spatial priors, such as HD maps. This fundamental dependency severely limits scalability, restricting open-ended generation capabilities to the finite scale of existing collected datasets. To break this bottleneck, we present OccSim, the first occupancy world model-driven 3D simulator. OccSim obviates the requirement for continuous logs or HD maps; conditioned only on a single initial frame and a sequence of future ego-actions, it can stably generate over 3,000 continuous frames, enabling the continuous construction of large-scale 3D occupancy maps spanning over 4 kilometers for simulation. This represents an >80x improvement in stable generation length over previous state-of-the-art occupancy world models. OccSim is powered by two modules: W-DiT based static occupancy world model and the Layout Generator. W-DiT handles the ultra-long-horizon generation of static environments by explicitly introducing known rigid transformations in architecture design, while the Layout Generator populates the dynamic foreground with reactive agents based on the synthesized road topology. With these designs, OccSim can synthesize massive, diverse simulation streams. Extensive experiments demonstrate its downstream utility: data collected directly from OccSim can pre-train 4D semantic occupancy forecasting models to achieve up to 67% zero-shot performance on unseen data, outperforming previous asset-based simulator by 11%. When scaling the OccSim dataset to 5x the size, the zero-shot performance increases to about 74%, while the improvement over asset-based simulators expands to 22.1%.
@article{liu2026occsim,
title = {OccSim: Multi-kilometer Simulation with Long-horizon Occupancy World Models},
author = {Liu, Tianran and Zhao, Shengwen and Pourkeshavarz, Mozhgan and Li, Weican and Rhinehart, Nicholas},
journal = {arXiv preprint arXiv:2603.28887},
year = {2026},
doi = {10.48550/arXiv.2603.28887},
url = {https://arxiv.org/abs/2603.28887}
}
AutoWorld: Scaling Multi-Agent Traffic Simulation with Self-Supervised World Models.
arXiv:2603.28963, (arXiv), 2026.
arXiv:2603.28963, (arXiv), 2026.
Multi-agent traffic simulation is central to developing and testing autonomous driving systems. Recent data-driven simulators have achieved promising results, but rely heavily on supervised learning from labeled trajectories or semantic annotations, making it costly to scale their performance. Meanwhile, large amounts of unlabeled sensor data can be collected at scale but remain largely unused by existing traffic simulation frameworks. This raises a key question: How can a method harness unlabeled data to improve traffic simulation performance? In this work, we propose AutoWorld, a traffic simulation framework that employs a world model learned from unlabeled occupancy representations of LiDAR data. Given world model samples, AutoWorld constructs a coarse-to-fine predictive scene context as input to a multi-agent motion generation model. To promote sample diversity, AutoWorld uses a cascaded Determinantal Point Process framework to guide the sampling processes of both the world model and the motion model. Furthermore, we designed a motion-aware latent supervision objective that enhances AutoWorld’s representation of scene dynamics. Experiments on the WOSAC benchmark show that AutoWorld ranks first on the leaderboard according to the primary Realism Meta Metric (RMM). We further show that simulation performance consistently improves with the inclusion of unlabeled LiDAR data, and study the efficacy of each component with ablations. Our method paves the way for scaling traffic simulation realism without additional labeling. Our project page contains additional visualizations and released code.
@article{pourkeshavarz2026autoworld,
title = {AutoWorld: Scaling Multi-Agent Traffic Simulation with Self-Supervised World Models},
author = {Pourkeshavarz, Mozhgan and Liu, Tianran and Rhinehart, Nicholas},
journal = {arXiv preprint arXiv:2603.28963},
year = {2026},
doi = {10.48550/arXiv.2603.28963},
url = {https://arxiv.org/abs/2603.28963}
}
Towards foundational LiDAR world models with efficient latent flow matching.
Advances in Neural Information Processing Systems, (NeurIPS), 2025.
Advances in Neural Information Processing Systems, (NeurIPS), 2025.
LiDAR-based world models offer more structured and geometry-aware representations than their image-based counterparts. However, existing LiDAR world models are narrowly trained; each model excels only in the domain for which it was built. This raises a critical question: can we develop LiDAR world models that exhibit strong transferability across multiple domains? To answer this, we conduct the first systematic domain transfer study across three demanding scenarios: (i) outdoor to indoor generalization, (ii) sparse- to dense-beam adaptation, and (iii) non-semantic to semantic transfer. Given different amounts of fine-tuning data, our experiments show that a single pretrained model can achieve up to 11% absolute improvement (83% relative) over training from scratch and outperforms training from scratch in 30/36 of our comparisons. This transferability significantly reduces the reliance on manually annotated data for semantic occupancy forecasting: our method exceeds previous baselines with only 5% of the labeled training data of prior work. We also observed inefficiencies of current generative-model-based LiDAR world models, mainly through their under-compression of LiDAR data and inefficient training objectives. To address these issues, we propose a latent conditional flow matching (CFM)-based framework that achieves state-of-the-art reconstruction accuracy using only half the training data and a compression ratio 6 times higher than that of prior methods. Our model also achieves SOTA performance on semantic occupancy forecasting while being 1.98x-23x more computationally efficient (a 1.1x-3.9x FPS speedup) than previous methods.
@article{liu2025foundationallidarworldmodels,
title={Towards foundational LiDAR world models with efficient latent flow matching},
author={Tianran Liu and Shengwen Zhao and Nicholas Rhinehart},
year={2025},
eprint={2506.23434},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.23434},
}
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation.
IEEE Robotics and Automation Letters, (RA-L), 2025.
IEEE Robotics and Automation Letters, (RA-L), 2025.
How can a robot safely navigate around people with complex motion patterns? Deep Reinforcement Learning (DRL) in simulation holds some promise, but much prior work relies on simulators that fail to capture the nuances of real human motion. Thus, we propose Deep Residual Model Predictive Control (DR-MPC) to enable robots to quickly and safely perform DRL from real-world crowd navigation data. By blending MPC with model-free DRL, DR-MPC overcomes the DRL challenges of large data requirements and unsafe initial behavior. DR-MPC is initialized with MPC-based path tracking, and gradually learns to interact more effectively with humans. To further accelerate learning, a safety component estimates out-of-distribution states to guide the robot away from likely collisions. In simulation, we show that DR-MPC substantially outperforms prior work, including traditional DRL and residual DRL models. Hardware experiments show our approach successfully enables a robot to navigate a variety of crowded situations with few errors using less than 4 hours of training data.
@article{han2024dr,
title={DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation},
author={Han, James R and Thomas, Hugues and Zhang, Jian and Rhinehart, Nicholas and Barfoot, Timothy D},
journal={IEEE Robotics and Automation Letters (RA-L)},
year={2025}
}
CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting.
European Conference on Computer Vision, (ECCV), 2024.
European Conference on Computer Vision, (ECCV), 2024.
We propose CARFF, a method for predicting future 3D scenes given past observations. Our method maps 2D ego-centric images to a distribution over plausible 3D latent scene configurations and predicts the evolution of hypothesized scenes through time. Our latents condition a global Neural Radiance Field (NeRF) to represent a 3D scene model, enabling explainable predictions and straightforward downstream planning. This approach models the world as a POMDP and considers complex scenarios of uncertainty in environmental states and dynamics. Specifically, we employ a two-stage training of Pose-Conditional-VAE and NeRF to learn 3D representations, and auto-regressively predict latent scene representations utilizing a mixture density network. We demonstrate the utility of our method in scenarios using the CARLA driving simulator, where CARFF enables efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving occlusions.
@article{yang2024carff,
author = {Yang, Jiezhi and Desai, Khushi and Packer, Charles and Bhatia, Harshil and Rhinehart, Nicholas and McAllister, Rowan and Gonzalez, Joseph},
journal = {arXiv preprint arXiv:2401.18075},
title = {CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting},
year = {2024}
}
The Waymo Open Sim Agents Challenge.
Advances in Neural Information Processing Systems, (NeurIPS), 2023.
Advances in Neural Information Processing Systems, (NeurIPS), 2023.
Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development. In this work, we introduce the Waymo Open Sim Agents Challenge (WOSAC). WOSAC is the first public challenge to tackle this task and propose corresponding metrics. The goal of the challenge is to stimulate the design of realistic simulators that can be used to evaluate and train a behavior model for autonomous driving. We outline our evaluation methodology, present results for a number of different baseline simulation agent methods, and analyze several submissions to the 2023 competition which ran from March 16, 2023 to May 23, 2023. The WOSAC evaluation server remains open for submissions and we discuss open problems for the task.
@article{montali2024waymo,
author = {Montali, Nico and Lambert, John and Mougin, Paul and Kuefler, Alex and Rhinehart, Nicholas and Li, Michelle and Gulino, Cole and Emrich, Tristan and Yang, Zoey and Whiteson, Shimon and others},
journal = {Advances in Neural Information Processing Systems},
title = {The waymo open sim agents challenge},
volume = {36},
year = {2024}
}
Is anyone there? learning a planner contingent on perceptual uncertainty.
Conference on Robot Learning, (CoRL), 2023.
Conference on Robot Learning, (CoRL), 2023.
Robots in complex multi-agent environments should reason about the intentions of observed and currently unobserved agents. In this paper, we present a new learning-based method for prediction and planning in complex multi-agent environments where the states of the other agents are partially-observed. Our approach, Active Visual Planning (AVP), uses high-dimensional observations to learn a flow-based generative model of multi-agent joint trajectories, including unobserved agents that may be revealed in the near future, depending on the robot’s actions. Our predictive model is implemented using deep neural networks that map raw observations to future detection and pose trajectories and is learned entirely offline using a dataset of recorded observations (not ground-truth states). Once learned, our predictive model can be used for contingency planning over the potential existence, intentions, and positions of unobserved agents. We demonstrate the effectiveness of AVP on a set of autonomous driving environments inspired by real-world scenarios that require reasoning about the existence of other unobserved agents for safe and efficient driving. In these environments, AVP achieves optimal closed-loop performance, while methods that do not reason about potential unobserved agents exhibit either overconfident or underconfident behavior.
@inproceedings{packer2023anyone,
author = {Packer, Charles and Rhinehart, Nicholas and McAllister, Rowan Thomas and Wright, Matthew A and Wang, Xin and He, Jeff and Levine, Sergey and Gonzalez, Joseph E},
booktitle = {Conference on Robot Learning},
organization = {PMLR},
pages = {1607--1617},
title = {Is anyone there? learning a planner contingent on perceptual uncertainty},
year = {2023}
}
S2Net: Stochastic Sequential Pointcloud Forecasting.
European Conference on Computer Vision, (ECCV), 2022.
European Conference on Computer Vision, (ECCV), 2022.
Predicting futures of surrounding agents is critical for autonomous systems such as self-driving cars. Instead of requiring accurate detection and tracking prior to trajectory prediction, an object agnostic Sequential Pointcloud Forecasting (SPF) task was proposed [28], which enables a forecast-then-detect pipeline effective for downstream detection and trajectory prediction. One limitation of prior work is that it forecasts only a deterministic sequence of future point clouds, despite the inherent uncertainty of dynamic scenes. In this work, we tackle the stochastic SPF problem by proposing a generative model with two main components: (1) a conditional variational recurrent neural network that models a temporally-dependent latent space; (2) a pyramid-LSTM that increases the fidelity of predictions with temporally-aligned skip connections. Through experiments on real-world autonomous driving datasets, our stochastic SPF model produces higher-fidelity predictions, reducing Chamfer distances by up to 56.6% compared to its deterministic counterpart. In addition, our model can estimate the uncertainty of predicted points, which can be helpful to downstream tasks.
@inproceedings{weng2022s2net,
author = {Weng, Xinshuo and Nan, Junyu and Lee, Kuan-Hui and McAllister, Rowan and Gaidon, Adrien and Rhinehart, Nicholas and Kitani, Kris},
booktitle = {European Conference on Computer Vision (ECCV)},
title = {S2Net: Stochastic Sequential Pointcloud Forecasting},
year = {2022}
}
Traffic prediction with reparameterized pushforward policy for autonomous vehicles.
U.S. Patent No. 11,189,171, 2021.
U.S. Patent No. 11,189,171, 2021.
Systems and methods for vehicle behavior prediction include an imaging device that captures images of a vehicle in traffic. A processing device including policy stored in a memory of the processing device in communication with the imaging device stochastically models future behavior of the vehicle based on the captured images. A policy simulator in communication with the processing device simulates the policy as a reparameterized pushforward policy of a base distribution. An evaluator receives the simulated policy from the policy simulator and performs cross-entropy optimization on the future behavior of the vehicle by analyzing the simulated policy and updating the policy according to cross-entropy error. An alert system retrieves the future behavior of the vehicle and recognizes hazardous trajectories of the future trajectories and generates an audible alert using a speaker.
@misc{vernaza2021traffic,
author = {Vernaza, Paul and Rhinehart, Nicholas},
month = {November~30},
note = {US Patent 11,189,171},
title = {Traffic prediction with reparameterized pushforward policy for autonomous vehicles},
year = {2021}
}
Contingencies from observations: Tractable contingency planning with learned behavior models.
IEEE International Conference on Robotics and Automation, (ICRA), 2021.
IEEE International Conference on Robotics and Automation, (ICRA), 2021.
Humans have a remarkable ability to accurately reason about future events, including the behaviors and states of mind of other agents. Consider driving a car through a busy intersection: it is necessary to reason about the physics of the vehicle, the intentions of other drivers, and their beliefs about your own intentions. For example, if you signal a turn, another driver might yield to you; or if you enter the passing lane, another driver might decelerate to give you room to merge in front. Competent drivers must plan how they can safely react to a variety of potential future behaviors of other agents before they make their next move. This requires contingency planning: explicitly planning a set of conditional actions that depend on the stochastic outcome of future events. In this work, we develop a general-purpose contingency planner that is learned end-to-end using high-dimensional scene observations and low-dimensional behavioral observations. We use a conditional autoregressive flow model for contingency planning. We show how this model can tractably learn contingencies from behavioral observations. We developed a closed-loop control benchmark of realistic multi-agent scenarios in a driving simulator (CARLA), on which we compare our method to various noncontingent methods that reason about multi-agent future behavior, and find that our contingency planning method achieves qualitatively and quantitatively superior performance.
@inproceedings{rhinehart2021contingencies,
author = {Rhinehart, Nicholas and He, Jeff and Packer, Charles and Wright, Matthew A and McAllister, Rowan and Gonzalez, Joseph E and Levine, Sergey},
booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
organization = {IEEE},
pages = {13663--13669},
title = {Contingencies from observations: Tractable contingency planning with learned behavior models},
year = {2021}
}
Generative adversarial inverse trajectory optimization for probabilistic vehicle forecasting.
U.S. Patent 10,739,773, 2020.
U.S. Patent 10,739,773, 2020.
Systems and methods for predicting vehicle behavior includes capturing images of a vehicle in traffic using an imaging device. Future behavior of the vehicle is stochastically modeled using a processing device including an energy-based model stored in a memory of the processing device. The energy-based model includes generating a distribution of possible future trajectories of the vehicle using a generator, sampling the distribution of possible future trajectories according to an energy value of each trajectory in the distribution of possible future trajectories an energy model to determine probable future trajectories, and optimizing parameters of each of the generator and the energy model using an optimizer. A user is audibly alerted with a speaker upon an alert system recognizing hazardous trajectories of the probable future trajectories.
@misc{vernaza2020generative,
author = {Vernaza, Paul and Choi, Wongun and Rhinehart, Nicholas},
month = {July~7},
note = {US Patent 10,705,531},
title = {Generative adversarial inverse trajectory optimization for probabilistic vehicle forecasting},
year = {2020}
}
Deep Imitative Models for Flexible Inference, Planning, and Control.
International Conference on Learning Representations, (ICLR), 2020.
International Conference on Learning Representations, (ICLR), 2020.
Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. Yet, reward functions that evoke desirable behavior are often difficult to specify. In this paper, we propose Imitative Models to combine the benefits of IL and goal-directed planning. Imitative Models are probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals. We derive families of flexible goal objectives, including constrained goal regions, unconstrained goal sets, and energy-based goals. We show that our method can use these objectives to successfully direct behavior. Our method substantially outperforms six IL approaches and a planning-based approach in a dynamic simulated autonomous driving task, and is efficiently learned from expert demonstrations without online data collection. We also show our approach is robust to poorly specified goals, such as goals on the wrong side of the road.
@inproceedings{rhinehart2020deep,
author = {Rhinehart, Nicholas and McAllister, Rowan and Levine, Sergey},
booktitle = {International Conference on Learning Representations (ICLR)},
title = {Deep Imitative Models for Flexible Inference, Planning, and Control},
year = {2020}
}
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings.
Proceedings of the IEEE International Conference on Computer Vision, (ICCV), 2019.
Best Paper Award @ ICML 2019 Workshop on AI for Autonomous Driving
Proceedings of the IEEE International Conference on Computer Vision, (ICCV), 2019.
Best Paper Award @ ICML 2019 Workshop on AI for Autonomous Driving
For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other drivers from rich perceptual information. Towards these capabilities, we present a probabilistic forecasting model of future interactions between a variable number of agents. We perform both standard forecasting and the novel task of conditional forecasting, which reasons about how all agents will likely respond to the goal of a controlled agent (here, the AV). We train models on real and simulated data to forecast vehicle trajectories given past positions and LIDAR. Our evaluation shows that our model is substantially more accurate in multi-agent driving scenarios compared to existing state-of-the-art. Beyond its general ability to perform conditional forecasting queries, we show that our model’s predictions of all agents improve when conditioned on knowledge of the AV’s goal, further illustrating its capability to model agent interactions.
@inproceedings{rhinehart2019precog,
author = {Rhinehart, Nicholas and McAllister, Rowan and Kitani, Kris and Levine, Sergey},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
title = {PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings},
year = {2019}
}
Jointly Forecasting and Controlling Behavior by Learning from High-Dimensional Data.
2019.
2019.
Achieving a precise predictive understanding of the future is difficult, yet widely studied in the natural sciences. Significant research activity has been dedicated to building testable models of cause and effect. From a certain view, the ability to forecast the universe is the “holy grail”; the ultimate goal of science. If we had it, we could anticipate, and therefore (at least implicitly) understand all observable phenomena. The human capability to forecast offers complementary motivation. Critical to our intelligence is our ability to plan behaviors by considering how our actions are likely to result in future payoff, especially in the presence of other collaborative and competitive agents. In this work, we seek to computationally model the future in the presence of agent behavior given rich observations of the environment. The brunt of our focus is to reason about what agents could do, instead of other sources of stochasticity. This focus on future agent behavior allows us to tightly couple and jointly perform forecasting and control. The field of Computer Vision (CV) is focused on designing algorithms to automatically understand images, videos, and other perceptual data. However, the field’s effort to-date focuses on non-interactive, present-focused tasks [79, 81, 158, 184]. Most CV contributions are algorithms to answer questions like “what is that”, and “what happened”, rather than “what could happen”, or “how could I achieve X”. Computer Vision has under-explored reasoning about the interactive and decision-based nature of the world. In contrast, Reinforcement Learning (RL) prioritizes modeling interactions and decisions by focusing on how to design algorithms to evoke behavior that maximizes a scalar reward signal. The resulting learning agents, in order to perform well, must have an understanding of how their current behaviors will affect their prospects of future reward. However, in the dominant paradigm of model-free RL [218], agents reason implicitly about the future. In contrast, model-based RL learns one-step dynamics to estimate “what could happen in the near future”. Yet model-based RL primarily focuses on control, rather than explicitly forecasting a single agent (let alone multiple agents). In this thesis, we consider the problem of designing algorithms to enable computational systems to (1) forecast future behavior of intelligent agents given rich observations of their environments, as well as to (2) use this reasoning for control. We believe these two problems should be tightly integrated and jointly considered, and use them to structure this thesis. We define forecasting to be the problem of estimating the set of possible outcomes of a system, whereas control is the problem of producing actions that generate a single outcome of a system. We often use Imitation Learning and Reinforcement Learning to formulate and situate our work. We contribute forecasting and control approaches to excel in diverse, realistic, single-agent, and multi-agent domains. The first part of the thesis focuses on progressively designing more capable forecasting models. We proceed through approaches to (1) forecast single actions of daily behavior by developing matrix factorization models [169], (2) forecast goal-driven action trajectories of daily behavior by developing Online Inverse Reinforcement Learning models [168, 170], (3) forecast motion trajectories of vehicles by developing a deep reversible generative models [171, 174]. The second part of the thesis focuses on progressively designing more capable models that tightly couple forecasting and control. We discuss (4) forecasting as auxiliary supervision for implicitly-planned control [228], (5) forecasting and explicitly planning with the same model [176], and (6) forecasting and planning future interactions of multiple agents [175].
@phdthesis{rhinehart2019jointly,
author = {Rhinehart, Nicholas},
school = {Carnegie Mellon University},
title = {Jointly Forecasting and Controlling Behavior by Learning from High-Dimensional Data},
year = {2019}
}
Generative Hybrid Representations for Activity Forecasting with No-Regret Learning.
Conference on Computer Vision and Pattern Recognition, (CVPR), 2019.
Conference on Computer Vision and Pattern Recognition, (CVPR), 2019.
Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems. Part of this difficulty stems from learning systems’ inability to represent all kinds of behaviors. Some behaviors, such as motion, are best described with continuous representations, whereas others, such as picking up a cup, are best described with discrete representations. Furthermore, human behavior is generally not fixed: people can change their habits and routines. This suggests these systems must be able to learn and adapt continuously. In this work, we develop an efficient deep generative model to jointly forecast a person’s future discrete actions and continuous motions. On a large-scale egocentric dataset, EPIC-KITCHENS, we observe our method generates high-quality and diverse samples while exhibiting better generalization than related generative models. Finally, we propose a variant to continually learn our model from streaming data, observe its practical effectiveness, and theoretically justify its learning efficiency.
@article{guan2019generative,
author = {Guan, Jiaqi and Yuan, Ye and Kitani, Kris M and Rhinehart, Nicholas},
journal = {arXiv preprint arXiv:1904.06250},
title = {Generative Hybrid Representations for Activity Forecasting with No-Regret Learning},
year = {2019}
}
R2P2: A Reparameterized Pushforward Policy for Diverse, Precise Generative Path Forecasting.
Proceedings of the European Conference on Computer Vision, (ECCV), 2018.
Proceedings of the European Conference on Computer Vision, (ECCV), 2018.
We propose a method to forecast a vehicle’s ego-motion as a distribution over spatiotemporal paths, conditioned on features (e.g., from LIDAR and images) embedded in an overhead map. The method learns a policy inducing a distribution over simulated trajectories that is both “diverse” (produces most of the likely paths) and “precise” (mostly produces likely paths). This balance is achieved through minimization of a symmetrized cross-entropy between the distribution and demonstration data. By viewing the simulated-outcome distribution as the pushforward of a simple distribution under a simulation operator, we obtain expressions for the cross-entropy metrics that can be efficiently evaluated and differentiated, enabling stochastic-gradient optimization. We propose concrete policy architectures for this model, discuss our evaluation metrics relative to previously-used degenerate metrics, and demonstrate the superiority of our method relative to state-of-the-art methods in both the Kitti dataset and a similar but novel and larger real-world dataset explicitly designed for the vehicle forecasting domain.
@inproceedings{rhinehart2018r2p2,
author = {Rhinehart, Nicholas and Kitani, Kris M. and Vernaza, Paul},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
pages = {772--788},
title = {R2P2: A Reparameterized Pushforward Policy for Diverse, Precise Generative Path Forecasting},
year = {2018}
}
First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence, (PAMI), 2018.
IEEE Transactions on Pattern Analysis and Machine Intelligence, (PAMI), 2018.
We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek. In contrast to prior work in trajectory forecasting, our algorithm, Darko, goes further to reason about semantic states (will I pick up an object?), and future goal states that are far in terms of both space and time. Darko learns and forecasts from first-person visual observations of the user’s daily behaviors via an Online Inverse Reinforcement Learning (IRL) approach. Classical IRL discovers only the rewards in a batch setting, whereas Darko discovers the transitions, rewards, and goals of a user from streaming data. Among other results, we show Darko forecasts goals better than competing methods in both noisy and ideal settings, and our approach is theoretically and empirically no-regret.
@article{rhinehart2018first,
author = {Rhinehart, Nicholas and Kitani, Kris},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher = {IEEE},
title = {First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning},
year = {2018}
}
Predictive-state decoders: Encoding the future into recurrent networks.
Advances in Neural Information Processing Systems, (NeurIPS), 2017.
Advances in Neural Information Processing Systems, (NeurIPS), 2017.
Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN. In the Predictive-State Representation (PSR) literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied on explicitly representing and targeting sufficient statistics of this probability distribution. We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network’s internal state representation to target predicting future observations. Predictive-State Decoders are simple to implement and easily incorporated into existing training pipelines via additional loss regularization. We demonstrate the effectiveness of PSDs with experimental results in three different domains: probabilistic filtering, Imitation Learning, and Reinforcement Learning. In each, our method improves statistical performance of state-of-the-art recurrent baselines and does so with fewer iterations and less data.
@inproceedings{venkatraman2017predictive,
author = {Venkatraman, Arun and Rhinehart, Nicholas and Sun, Wen and Pinto, Lerrel and Hebert, Martial and Boots, Byron and Kitani, Kris M. and Bagnell, J. A.},
booktitle = {Advances in Neural Information Processing Systems},
pages = {1172--1183},
title = {Predictive-state decoders: Encoding the future into recurrent networks},
year = {2017}
}
First-Person Activity Forecasting with Online Inverse Reinforcement Learning.
The IEEE International Conference on Computer Vision, (ICCV), 2017.
Best Paper Honorable Mention
The IEEE International Conference on Computer Vision, (ICCV), 2017.
Best Paper Honorable Mention
We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek. In contrast to prior work in trajectory forecasting, our algorithm, DARKO, goes further to reason about semantic states (will I pick up an object?), and future goal states that are far in terms of both space and time. DARKO learns and forecasts from first-person visual observations of the user’s daily behaviors via an Online Inverse Reinforcement Learning (IRL) approach. Classical IRL discovers only the rewards in a batch setting, whereas DARKO discovers the states, transitions, rewards, and goals of a user from streaming data. Among other results, we show DARKO forecasts goals better than competing methods in both noisy and ideal settings, and our approach is theoretically and empirically no-regret.
@inproceedings{rhinehart2017first,
author = {Rhinehart, Nicholas and Kitani, Kris M.},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
pages = {3716--3725},
title = {First-Person Activity Forecasting with Online Inverse Reinforcement Learning},
year = {2017}
}