8 papers
BooST: Bridging Semantics and Motions for Efficient Skill Transfer
Jusuk Lee, Daesol Cho, Jonghun Shin +4
Skill abstraction---the process of learning reusable and temporally extended behaviors---has emerged as a key paradigm for improving sample efficiency and generalization in robot l…
FLAG: Flow Policy MaxEnt-RL by Latent Augmented Guidance
Sungha Kim, Gawon Lee, Jusuk Lee +3
Maximum entropy reinforcement learning (MaxEnt-RL) enables robust exploration, yet practical implementations often restrict policies to simple Gaussians. While recent approaches in…
DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation
Jusuk Lee, Seungjae Lee, Jonghun Shin +6
Robot manipulation critically depends on perception that preserves the action-relevant aspects of a scene. Yet most robot learning pipelines are built upon visual encoders pre-trai…
EgoAVFlow: Robot Policy Learning with Active Vision from Human Egocentric Videos via 3D Flow
Daesol Cho, Youngseok Jang, Danfei Xu +1
Egocentric human videos provide a scalable source of manipulation demonstrations; however, deploying them on robots requires active viewpoint control to maintain task-critical visi…
Temporal Action Representation Learning for Tactical Resource Control and Subsequent Maneuver Generation
Hoseong Jung, Sungil Son, Daesol Cho +3
Autonomous robotic systems should reason about resource control and its impact on subsequent maneuvers, especially when operating with limited energy budgets or restricted sensing.…
AdaptManip: Learning Adaptive Whole-Body Object Lifting and Delivery with Online Recurrent State Estimation
Morgan Byrd, Donghoon Baek, Kartik Garg +5
This paper presents Adaptive Whole-body Loco-Manipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated navigation, object lifting, and delive…