36 papers
Demonstration-Based Robotic Feeding Using Dynamic Movement Primitives
Kenzhi Iskandar Wong, Kitti Thamrongaphichartkul, Sri Harsha Turlapati +10
This paper presents a demonstration-based robotic feeding framework that integrates human demonstration, Dynamic Movement Primitive-based motion planning, vision-based scene percep…
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…
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…
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes
Lisong He, Yunxuan Jiang, Haishan Ye +1
Influence functions enable principled data selection for large language models (e.g., identifying influential training samples for model editing, debugging, or efficient fine-tunin…
FreeSpeed: Training-Free Speed Control for Generative Robot Policies
Yuxuan Hu, Shilin Shan, Qiheng Wang +4
Online control of execution speed is essential for deploying robot policies in real-world scenarios, as robots may need to speed up under time constraints or slow down to facilitat…
TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training
Ning Zhu, Mengfei Zhao, Yikai Tang +5
Simulation demonstrations can supplement scarce real-world data for robot policy co-training. However, the value of using data curation to actively select these demonstrations for…
Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing
Yuxuan Hu, Shilin Shan, Jianfei Yang +1
Contactless heart-rate sensing with millimeter-wave (mmWave) radar requires assessing whether individual measurements support reliable estimation. We study learning to assess heart…
LIBERO-MAX: Do Robot Policies Adapt When the World Changes?
Yunbei Zhang, Zijian Jin, Yuanzhe Liu +14
Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and committed actions reflect the previ…
RoboDreamer: Anticipatory Humanoid Locomotion with Predictive State-Space Models
Zhe Li, Yangyang Wei, Xichen Yuan +4
Humanoid locomotion requires control policies that remain stable under imperfect sensing while exploiting temporal context for consistent motion. We present RoboDreamer, a two-stag…
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…
-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation
Zhe Li, Zhenzhe Zhang, Yangyang Wei +8
Humanoid household tasks often require concurrent loco-manipulation, where the robot must move, adjust posture, maintain balance, and manipulate objects as a single coordinated beh…
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…
AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation
Mengfei Zhao, Dihong Huang, Yikai Tang +12
Learning effective robot manipulation policies requires diverse, high-quality demonstrations, yet existing data pipelines are often difficult to scale because they rely on speciali…
FuncBridge: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning
Chuhao Zhou, Liquan Wang, Shuxin Cao +5
While humans readily repurpose a book, a stone, or a shoe to drive a nail, robots trained on specific tools fail to transfer the same function to novel ones -- a gap we formalize a…
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…
USS: Unifying Spatial-Semantic Prompting for End to End Embodied Visual Tracking
Yuchen Xie, Xinyu Zhou, Kuangji Zuo +4
Embodied Visual Tracking (EVT) requires an agent to continuously follow a designated target while moving through dynamic environments. Existing embodied tracking methods generally…
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…
APEX: Adaptive Policy Execution for Precise Manipulation
Mengfei Zhao, Chenxi Jiang, Tuo An +2
Modern imitation learning methods, including visuomotor and Vision-Language-Action (VLA) policies, typically output high-level action references that are executed by low-level cont…
Rethinking Implicit Spatial Representation in Visuomotor Policy Learning
Xiangyu Chen, Yuxuan Hu, Chuhao Zhou +1
Generative model-based imitation learning has become a widely adopted paradigm for robotic manipulation, where policy performance depends critically on the conditioned visual repre…
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…