12 papers
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Shilin Shan, Chuhao Zhou, Ruize Wang +30
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical…
Heterogeneous Tactile Transformer
Jianxin Bi, Qiang Wang, Jayaram Reddy +4
Tactile sensors are inherently heterogeneous: a model trained on one sensor cannot be directly used on another, which limits learning contact-rich manipulation policies from divers…
Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
Xuehui Yu, Fucheng Cai, Meiyi Wang +2
Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a controllable trajectory. This provi…
Guided Streaming Stochastic Interpolant Policy
Puming Jiang, Meiyi Wang, Kelvin Lin +2
Inference-time guidance is essential for steering generative robot policies toward dynamic objectives without retraining, yet existing methods are largely confined to chunk-based a…
Grounded World Model for Semantically Generalizable Planning
Quanyi Li, Lan Feng, Haonan Zhang +4
In Model Predictive Control (MPC), world models predict the future outcomes of various action proposals, which are then scored to guide the selection of the optimal action. For vis…
SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse
Xuanran Zhai, Zekai Huang, Longyan Wu +5
Recent progress in vision-language-action (VLA) models has demonstrated strong potential for dual-arm manipulation, enabling complex behaviors and generalization to unseen environm…