SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from
  Sparse and Noisy Demonstrations
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.
681992911e0fae3880173d43
Runyi Yu
admin_assigned
2025-05-06T09:00:09.333Z
66d59dc9b005ad82ca6fc61d
/avatars/0ba424690afd1144a89665c5bacdfde7.svg
Runyi YU
user
IngridYU
681992911e0fae3880173d44
Yinhuai Wang
681992911e0fae3880173d45
Qihan Zhao
admin_assigned
2025-05-06T09:00:32.182Z
64341911546e16f17a129733
/avatars/ae12aafc8932a7537838e6d3964858cb.svg
QiHan Zhao
user
Crimnos
681992911e0fae3880173d46
Hok Wai Tsui
681992911e0fae3880173d47
Jingbo Wang
681992911e0fae3880173d48
Ping Tan
681992911e0fae3880173d49
Qifeng Chen
admin_assigned
2025-05-06T09:01:21.751Z
6467b121e7a6a374fd19b44b
/avatars/3f2874d58986d651aef55e3408b05700.svg
Qifeng Chen
user
cqf
SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from
  Sparse and Noisy Demonstrations
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.
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.
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