From the 1 of 29 linked papers with an AI index.
29 papers
Probabilistic Reachable-Action Verification of Visuomotor Policies via Set-Based Training
Yanliang Huang, Zhuocheng Zhang, Peng Xie +5
Reachability analysis for visuomotor policies is difficult because large visual encoders make end-to-end set propagation computationally expensive and excessively conservative. We…
PAC-DP: PAC-Bayesian Diffusion Policy Learning
Mohammad Hasan Yeganegi, Dian Yu, Andrea Del Prete +2
Diffusion Policies (DPs) are able to perform complex manipulation tasks. However, DPs are typically trained by minimizing a denoising objective, which provides limited control over…
FARO: Feasibility-Aware Robot Motion Optimization
Michal Ciebielski, Shafeef Omar, Aaron Johnson +1
Fast planning of novel behaviors in unseen scenarios remains a fundamental challenge in robotics. The high-dimensional, hybrid, and underactuated nature of humanoid loco-manipulati…
Safe-Night VLA: Seeing the Unseen via Thermal-Perceptive Vision-Language-Action Models for Safety-Critical Manipulation
Dian Yu, Qingchuan Zhou, Bingkun Huang +2
The paper introduces Safe-Night VLA, a robot manipulation system that combines long-wave infrared thermal sensing with a vision‑language backbone and adds safety guarantees via con…
SafeExplorer: An Unbiased Policy Gradient for Reinforcement Learning with Recovery Interventions
Elham Daneshmand, Majid Khadiv, Glen Berseth +1
Training reinforcement-learning agents directly on physical robots makes every fall costly, since a fall can damage the platform and cannot be undone like a simulator reset; the go…
Humanoid-DART: Humanoid Loco-Manipulation using Diffusion-guided Augmentation through Relabeling and Tracking
Pranav Debbad, Kanish Thiagarajan, Victor Dhédin +2
Imitating human demonstrations has emerged as a dominant paradigm for learning humanoid loco-manipulation policies. However, scaling these approaches remains challenging due to the…