9 papers · 1 filter
PACER: Process-Aware VLA Policy Learning from Human Corrections for Robotic Disassembly
Chang Liu, Sibo Tian, Litian Gong +4
Robotic disassembly of end-of-life (EoL) products requires robots to accommodate product variation, follow prerequisite sequences, and approach components precisely for contact-ric…
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study
Mingxuan Yan, Litian Gong, Dihong Huang +19
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and…
Training-Free Multi-Agent Cooperative Embodied Search from Uncertain Vision-Language Priors
Fan Pu, Keshu Wu, Jiwan Jiang +5
Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations may be incomplete or incorr…
Disentangling Task-Relevant Latent Dynamics for Reward Modeling
Dayou Li, Jionghao Wang, Peihao Wang +9
Reward design remains a major bottleneck for reinforcement learning in robotic manipulation. Handcrafted dense rewards can compensate for the limited guidance provided by sparse re…
RoboIRS: Inference-Time Internal Representation Steering for Generalist Robot Policies
Jiuzhou Lei, Chang Liu, Dayou Li +5
Vision-language-action (VLA) and world-action models (WAMs) often degrade under out-of-distribution task variations despite retaining partial task capability. To recover such capab…
Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering
Hao Wang, Jiuzhou Lei, Dayou Li +5
Behavior-cloned policies often learn multiple behavior modes from demonstration datasets, including modes that are unsafe or otherwise undesired at deployment. For example, a polic…
Embedding Semantic Risk into Distance Fields and CBFs for Online Monocular Safe Control
Dawei Zhang, Nuo Chen, Shuo Liu +2
We propose an online monocular perception-to-control framework that embeds semantic risk into the distance field used by Control Barrier Function (CBF)-based safe navigation and te…
Learning Actionable Manipulation Recovery via Counterfactual Failure Synthesis
Dayou Li, Jiuzhou Lei, Hao Wang +6
While recent foundation models have significantly advanced robotic manipulation, these systems still struggle to autonomously recover from execution errors. Current failure-learnin…
NavTrust: Benchmarking Trustworthiness for Embodied Navigation
Huaide Jiang, Yash Chaudhary, Yuping Wang +8
There are two major categories of embodied navigation: Vision-Language Navigation (VLN), where agents navigate by following natural language instructions; and Object-Goal Navigatio…