Search Results for author:
Found 19 papers, 9 papers with code
CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting
We demonstrate the utility of our method in realistic scenarios using the CARLA driving simulator, where CARFF can be used to enable efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving visual occlusions.
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States
Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems.
RAP: Risk-Aware Prediction for Robust Planning
Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions.
Control-Aware Prediction Objectives for Autonomous Driving
Autonomous vehicle software is typically structured as a modular pipeline of individual components (e. g., perception, prediction, and planning) to help separate concerns into interpretable sub-tasks.
Dynamics-Aware Comparison of Learned Reward Functions
The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world.
Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty
Reasoning about the future behavior of other agents is critical to safe robot navigation.
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models
Humans have a remarkable ability to make decisions by accurately reasoning about future events, including the future behaviors and states of mind of other agents.
Outcome-Driven Reinforcement Learning via Variational Inference
While reinforcement learning algorithms provide automated acquisition of optimal policies, practical application of such methods requires a number of design decisions, such as manually designing reward functions that not only define the task, but also provide sufficient shaping to accomplish it.
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions.
Learning Invariant Representations for Reinforcement Learning without Reconstruction
We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction.
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
Our experiments demonstrate that our online adaptation approach outperforms non-adaptive methods on a series of challenging suspended payload transportation tasks.
Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks
Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging.
Model-based Reinforcement Learning
reinforcement-learning
+1
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings
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.
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty
Our method estimates an uncertainty measure about the model's prediction, taking into account an explicit (generative) model of the observation distribution to handle out-of-distribution inputs.
Deep Imitative Models for Flexible Inference, Planning, and Control
Yet, reward functions that evoke desirable behavior are often difficult to specify.
Unsupervised Exploration with Deep Model-Based Reinforcement Learning
We show that both challenges can be addressed by representing model-uncertainty, which can both guide exploration in the unsupervised phase and ensure that the errors in the model are not exploited by the planner in the goal-directed phase.
Model-based Reinforcement Learning
reinforcement-learning
+1
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance.
Model-based Reinforcement Learning
reinforcement-learning
+1
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs
This enables data-efficient learning under significant observation noise, outperforming more naive methods such as post-hoc application of a filter to policies optimised by the original (unfiltered) PILCO algorithm.
Data-Efficient Reinforcement Learning in Continuous-State POMDPs
We present a data-efficient reinforcement learning algorithm resistant to observation noise.
