116 citations · 208 across the 5 of their papers we have counts for
13 papers
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models
Nicholas Rhinehart, Jeff He, Charles Packer +4
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. Consider driving…
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Avi Singh, Huihan Liu, Gaoyue Zhou +3
Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn.…
Conservative Safety Critics for Exploration
Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart +3
Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these pol…
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigas, Rowan McAllister +3
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In pr…
Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting
Xinshuo Weng, Jianren Wang, Sergey Levine +2
Many autonomous systems forecast aspects of the future in order to aid decision-making. For example, self-driving vehicles and robotic manipulation systems often forecast future ob…
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings
Nicholas Rhinehart, Rowan McAllister, Kris Kitani +1
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…