Search Results for author:
Found 4 papers, 3 papers with code
UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction
Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their generalization remain under-explored.
Ranked #1 on
Trajectory Prediction
on nuScenes
(using extra training data)
SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers
In this paper, we introduce a framework that can generate plausible human grasping motions suitable for training the robot.
Human-AI Shared Control via Policy Dissection
Inspired by the neuroscience approach to investigate the motor cortex in primates, we develop a simple yet effective frequency-based approach called \textit{Policy Dissection} to align the intermediate representation of the learned neural controller with the kinematic attributes of the agent behavior.
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
Based on MetaDrive, we construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic.
