From the 1 of 5 linked papers with an AI index.
5 papers
EDAR: Learning Environment-Dependent Action Representations for Robotic Manipulation
Yuecheng Xu, Tong Yang, Jingkai Jia +3
The paper introduces EDAR, a method that learns action representations for robotic manipulation by linking control commands with the visual effects they cause in a given environmen…
Context as Prior: Bayesian-Inspired Intent Inference for Non-Speaking Agents with a Household Cat Testbed
Wenqian Zhang, Zehao Wang
Many agents in real-world environments cannot reliably communicate their goals through language, including household pets, pre-verbal infants, and other non-speaking embodied agent…
LongBench: Evaluating Robotic Manipulation Policies on Real-World Long-Horizon Tasks
Xueyao Chen, Jingkai Jia, Tong Yang +3
Robotic manipulation policies often degrade over extended horizons, yet existing benchmarks provide limited insight into why such failures occur. Most prior benchmarks are either s…
Fast Visuomotor Policy for Robotic Manipulation
Jingkai Jia, Tong Yang, Xueyao Chen +2
We present a fast and effective policy framework for robotic manipulation, named Energy Policy, designed for high-frequency robotic tasks and resource-constrained systems. Unlike e…
Predicting 3D representations for Dynamic Scenes
Di Qi, Tong Yang, Beining Wang +2
We present a novel framework for dynamic radiance field prediction given monocular video streams. Unlike previous methods that primarily focus on predicting future frames, our meth…