6 papers
A4A: Cross-Embodiment Transfer of Action-Oriented 4D Affordances from Human Demonstrations
Yifan Han, Litao Liu, Yuqi Gu +7
Human demonstrations contain rich manipulation knowledge, but it remains unclear what information can be transferred effectively to robot control. Existing affordance representatio…
Affordance2Action: Task-Conditioned Scene-level Affordance Grounding for Real-Time Manipulation
Litao Liu, Yifan Han, Pengfei Yi +9
Task-conditioned manipulation requires grounding instructions to task-relevant functional parts rather than object categories. This setting is scene-dependent and often one-to-many…
Toward Holistic Planning and Control Optimization for Dual-Arm Rearrangement
Kai Gao, Zihe Ye, Duo Zhang +2
Long-horizon task and motion planning (TAMP) is notoriously difficult to solve, let alone optimally, due to the tight coupling between the interleaved (discrete) task and (continuo…
EARL: Eye-on-Hand Reinforcement Learner for Dynamic Grasping with Active Pose Estimation
Baichuan Huang, Jingjin Yu, Siddarth Jain
In this paper, we explore the dynamic grasping of moving objects through active pose tracking and reinforcement learning for hand-eye coordination systems. Most existing vision-bas…
Toward Optimal Tabletop Rearrangement with Multiple Manipulation Primitives
Baichuan Huang, Xujia Zhang, Jingjin Yu
In practice, many types of manipulation actions (e.g., pick-n-place and push) are needed to accomplish real-world manipulation tasks. Yet, limited research exists that explores the…
ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star
Kai Gao, Zhaxizhuoma, Yan Ding +2
Effectively performing object rearrangement is an essential skill for mobile manipulators, e.g., setting up a dinner table or organizing a desk. A key challenge in such problems is…