Adapting robot manipulation policies to new tasks and environments remains highly data-intensive, while the data needed for further improvement depends on the policy's current capabilities and failure modes. We introduce EmbodiRSI, an agentic system for recursive self-improvement (RSI) in a real-to-sim-to-real setting,...
Hao-Ran Lang, Hao-Tao Lu, Shi-Yu Sang et al.· 1 citation
This work proposes SCRIPT, a scalable diffusion policy with a multi-stage training framework for language-driven physics-based humanoid control, and introduces a nonlinear history conditioning mechanism, which preserves the dense recent context and samples increasingly sparse cues from long-term history.
Jingyan Zhang, Han Liang, Rui-Chi Zhang et al.· arXiv.org· 5 citations· ⚡2
LabEvolver couples a state-grounded inner trial loop for adaptive perception, online planning, and safety validation with an outer evolution loop that distills completed trajectories into reusable skill, strategy, and safety experience, showing generality beyond wet-lab settings.
SelfWAM is introduced, a unified self-grounded WAM built on a modality-specialized Mixture-of-Transformers (MoT) architecture that jointly predicts actions, action-conditioned future RGB frames, and robot self-masks, thereby grounding future prediction in the robot's visible body and its action-induced motion.
Bikang Pan, Fan Liu, Haotao Lu et al.· 5 citations
TactiDex is introduced, a real-world tactile-guided benchmark specifically designed to move dexterous manipulation beyond kinematic mimicry toward contact-level human-likeness and TactiSkill, a framework built upon a novel tri-component tactile reward that innovatively uses tactile signals as structured supervision.
Suting Ni, Hanbing Zhang, Zhenyu Wei et al.· 3 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
