DexCompose achieves 77.4% average success on 16 composite dexterous tasks by using role-aware residual composition with explicit finger ownership to combine pretrained policies without destructive interference.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
AnyBody distills a privileged teacher tracker into a latent unit-sphere representation and uses a masked transformer to drive humanoid control from arbitrary keypoint subsets.
DPPO fine-tunes diffusion policies via policy gradients and outperforms prior RL approaches for diffusion policies and PG-tuned alternatives on robot benchmarks while enabling stable training and hardware deployment.
TacCoRL integrates tactile feedback into VLA policies via real-aligned simulation co-training and RL, raising average success from 50% to 72.5% on four bimanual contact-rich tasks with direct real-robot transfer.
A model-free RL method arbitrates between a functional baseline policy and a learning policy, transferring agency over time to yield a standalone policy with high goal-reaching rates and competitive returns on continuous-control tasks.
citing papers explorer
-
DexCompose: Reusing Dexterous Policies for Multi-Task Manipulation with a Single Hand
DexCompose achieves 77.4% average success on 16 composite dexterous tasks by using role-aware residual composition with explicit finger ownership to combine pretrained policies without destructive interference.
-
AnyBody: Free-Form Whole-Body Humanoid Control from Arbitrary Keypoint Guidance
AnyBody distills a privileged teacher tracker into a latent unit-sphere representation and uses a masked transformer to drive humanoid control from arbitrary keypoint subsets.
-
Diffusion Policy Policy Optimization
DPPO fine-tunes diffusion policies via policy gradients and outperforms prior RL approaches for diffusion policies and PG-tuned alternatives on robot benchmarks while enabling stable training and hardware deployment.
-
TacCoRL: Integrating Tactile Feedback into VLA via Simulation
TacCoRL integrates tactile feedback into VLA policies via real-aligned simulation co-training and RL, raising average success from 50% to 72.5% on four bimanual contact-rich tasks with direct real-robot transfer.
-
An Agency-Transferring Model-Free Policy Enhancement Technique
A model-free RL method arbitrates between a functional baseline policy and a learning policy, transferring agency over time to yield a standalone policy with high goal-reaching rates and competitive returns on continuous-control tasks.