title: SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from
    Sparse and Noisy Demonstrations
summary: We address a fundamental challenge in Reinforcement Learning from Interaction
  Demonstration (RLID): demonstration noise and coverage limitations. While
  existing data collection approaches provide valuable interaction
  demonstrations, they often yield sparse, disconnected, and noisy trajectories
  that fail to capture the full spectrum of possible skill variations and
  transitions. Our key insight is that despite noisy and sparse demonstrations,
  there exist infinite physically feasible trajectories that naturally bridge
  between demonstrated skills or emerge from their neighboring states, forming a
  continuous space of possible skill variations and transitions. Building upon
  this insight, we present two data augmentation techniques: a Stitched
  Trajectory Graph (STG) that discovers potential transitions between
  demonstration skills, and a State Transition Field (STF) that establishes
  unique connections for arbitrary states within the demonstration neighborhood.
  To enable effective RLID with augmented data, we develop an Adaptive Trajectory
  Sampling (ATS) strategy for dynamic curriculum generation and a historical
  encoding mechanism for memory-dependent skill learning. Our approach enables
  robust skill acquisition that significantly generalizes beyond the reference
  demonstrations. Extensive experiments across diverse interaction tasks
  demonstrate substantial improvements over state-of-the-art methods in terms of
  convergence stability, generalization capability, and recovery robustness.
paper_authors._id: 681992911e0fae3880173d43
paper_authors.name: Runyi Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:00:09.333Z
paper_authors._id: 66d59dc9b005ad82ca6fc61d
paper_authors.avatarUrl: /avatars/0ba424690afd1144a89665c5bacdfde7.svg
paper_authors.fullname: Runyi YU
paper_authors.type: user
paper_authors.user: IngridYU
paper_authors._id: 681992911e0fae3880173d44
paper_authors.name: Yinhuai Wang
paper_authors._id: 681992911e0fae3880173d45
paper_authors.name: Qihan Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:00:32.182Z
paper_authors._id: 64341911546e16f17a129733
paper_authors.avatarUrl: /avatars/ae12aafc8932a7537838e6d3964858cb.svg
paper_authors.fullname: QiHan Zhao
paper_authors.type: user
paper_authors.user: Crimnos
paper_authors._id: 681992911e0fae3880173d46
paper_authors.name: Hok Wai Tsui
paper_authors._id: 681992911e0fae3880173d47
paper_authors.name: Jingbo Wang
paper_authors._id: 681992911e0fae3880173d48
paper_authors.name: Ping Tan
paper_authors._id: 681992911e0fae3880173d49
paper_authors.name: Qifeng Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:01:21.751Z
paper_authors._id: 6467b121e7a6a374fd19b44b
paper_authors.avatarUrl: /avatars/3f2874d58986d651aef55e3408b05700.svg
paper_authors.fullname: Qifeng Chen
paper_authors.type: user
paper_authors.user: cqf
paper_title: SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from
    Sparse and Noisy Demonstrations
paper_summary: We address a fundamental challenge in Reinforcement Learning from Interaction
  Demonstration (RLID): demonstration noise and coverage limitations. While
  existing data collection approaches provide valuable interaction
  demonstrations, they often yield sparse, disconnected, and noisy trajectories
  that fail to capture the full spectrum of possible skill variations and
  transitions. Our key insight is that despite noisy and sparse demonstrations,
  there exist infinite physically feasible trajectories that naturally bridge
  between demonstrated skills or emerge from their neighboring states, forming a
  continuous space of possible skill variations and transitions. Building upon
  this insight, we present two data augmentation techniques: a Stitched
  Trajectory Graph (STG) that discovers potential transitions between
  demonstration skills, and a State Transition Field (STF) that establishes
  unique connections for arbitrary states within the demonstration neighborhood.
  To enable effective RLID with augmented data, we develop an Adaptive Trajectory
  Sampling (ATS) strategy for dynamic curriculum generation and a historical
  encoding mechanism for memory-dependent skill learning. Our approach enables
  robust skill acquisition that significantly generalizes beyond the reference
  demonstrations. Extensive experiments across diverse interaction tasks
  demonstrate substantial improvements over state-of-the-art methods in terms of
  convergence stability, generalization capability, and recovery robustness.
paper_ai_summary: The approach uses data augmentation techniques and adaptive sampling to improve robust skill acquisition and generalization in Reinforcement Learning from Interaction Demonstration despite noisy and sparse demonstrations.
paper_ai_keywords: Reinforcement Learning from Interaction Demonstration (RLID) demonstration noise coverage limitations sparse trajectories skill variations transitions physically feasible trajectories Stitched Trajectory Graph (STG) State Transition Field (STF) Adaptive Trajectory Sampling (ATS) dynamic curriculum generation memory-dependent skill learning convergence stability generalization capability recovery robustness