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Flow Matching for Robot Foundation Models
Kuangji Zuo, Tuo An, Xinying Guo +9
Flow matching is a common generative recipe for robot foundation models. Vision-language-action models (VLAs) generate action chunks, while world-action models (WAMs) additionally…
Unified Condition-Action Modeling for Accurate One-Step Action Generation
Xinyu Zhou, Zikun Cai, Kuangji Zuo +7
Robot manipulation requires policies that are both accurate and efficient, as robot control must respond to changing observations under tight latency constraints. Recent diffusion…
Physics Filtering Favors the Generalization of Robot Learning
Jindou Jia, Shixuan Han, Meng Wang +8
Living organisms exhibit extraordinary adaptability to unseen environments through their intrinsic physical structures and lifelong feedback-driven learning. Endowing robots with c…
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…
ActiveVital: Geometry-Aware Embodied Vital Signs Monitoring for Home Healthcare Robots
Yuxuan Hu, Shihao Li, Yang Xiao +3
Home robots require reliable vital signs monitoring to support long-term companionship and safety in daily environments, yet obtaining respiration and heart rate without physical c…
ADAPT: Analytical Disturbance-Aware Policy Training for Humanoid Locomotion
Bofan Lyu, Jindou Jia, Kuangji Zuo +7
Humanoids deployed in human-centered environments must handle force-interactive tasks, where external contacts introduce unexpected disturbances that disrupt locomotion accuracy an…
GIVE: Grounding Human Gestures in Vision-Language-Action Models
Pengfei Liu, Gen Li, Junqiao Fan +4
Human communication is inherently multimodal, where language is often accompanied by non-verbal cues such as gestures to convey intentions. However, current Vision-Language-Action…
Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation
Kuangji Zuo, Gen Li, Bofan Lyu +9
Vision-Language-Action (VLA) models have recently shown strong potential for robot learning by following language instructions. However, in practice, language alone is often insuff…
MARS Policy: Multimodality Only When It Matters
Jindou Jia, Tuo An, Yuxuan Hu +7
Imitation learning has become a cornerstone for solving complex robotic manipulation tasks. In particular, multimodality, which enables robots to capture diverse yet valid behavior…
OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning
Geng Li, Guohao Chen, Ting Chen +6
Vision-language models (VLMs) rely on long visual token sequences for visual understanding, making the prefill stage expensive in both computation and memory. Most existing pruning…
CompassAD: Intent-Driven 3D Affordance Grounding in Functionally Competing Objects
Jingliang Li, Jindou Jia, Tuo An +7
When told to "cut the cake," a robot must choose the knife over nearby scissors, despite both objects affording the same cutting function. In real-world scenes, multiple objects ma…
Feedback World Model Enables Precise Guidance of Diffusion Policy
Tuo An, Jindou Jia, Gen Li +8
World models aim to improve robotic decision making by predicting the consequences of actions. However, in practice, their predictions often become unreliable once the robot encoun…
FLASH: Efficient Visuomotor Policy via Sparse Sampling
Jiaqi Bai, Jindou Jia, Yuxuan Hu +5
Generative models such as diffusion and flow matching have become dominant paradigms for visuomotor policy learning, yet their reliance on iterative denoising incurs high inference…