15 papers
Do My Samples Help? Compute Budget Allocation for Real-World Sampling-Based MPC in Contact-Rich Manipulation
Magnus Dierking, João Carvalho, Vince Kurtz +3
Sampling-based model predictive control (SMPC) is increasingly practical for contact-rich manipulation because GPU-based physics simulators enable real-time evaluation of large num…
Exploiting Hierarchical Controller Structure in Contextual Parameter Learning for Humanoid Loco-Manipulation
Sebastian Hirt, Lukas Theiner, Jan Peters +1
Hierarchical control architectures are widely used to decompose complex control problems into interacting control levels and are particularly important in robotics, where planning,…
Residual Compliant Policy: Learning Compliant Policies from Few Demonstrations
Chunyu Jiang, Yichen Cai, Shuai Li +6
Contact-rich manipulation requires a robot to be precise in free space to establish the intended contacts, and compliant once in contact to avoid excessive forces. Imitation learni…
CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments
Tan-Dzung Do, Tuan Dat Phuong, Nico Bohlinger +6
Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to imitate, a pose to reach, or…
Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
Zechu Li, Yufeng Jin, Puze Liu +2
A key bottleneck in training generalist policies for bimanual dexterous manipulation is the lack of large-scale, high-quality datasets. Synthetic data generation in simulation prov…
Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha +18
Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose pro…
Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation
Magnus Dierking, Joao Carvalho, An Thai Le +2
Sampling-based Model Predictive Control (SMPC) is a promising strategy for contact-rich robotic manipulation, combining gradient-free optimization with massively parallel GPU simul…
Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing
Duc Minh Nguyen, Bao-Ngoc Dao, Tung M. Luu +15
Diffusion-based Vision-Language-Action (VLA) policies enable strong generalization in robotic manipulation, but remain sensitive to spurious visual correlations and noisy action ge…
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
Alessandro Montenegro, Shihao Li, Puze Liu +2
Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately…
Learning Sim-Grounded Policies for Bimanual Rope Manipulation from Human Teleoperation Data
Gina Wigginghaus, Tim Missal, Berk Guler +2
Deformable Linear Objects (DLOs) such as ropes and cables are widely encountered in both household and industrial applications, yet remain challenging to manipulate due to their in…
DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion
Khang Nguyen, An T. Le, Jan Peters +1
Achieving robust robot learning for humanoid locomotion is a fundamental challenge in model-based reinforcement learning (MBRL), where environmental stochasticity and randomness ca…
TD-GRPC: Temporal Difference Learning with Group Relative Policy Constraint for Humanoid Locomotion
Khang Nguyen, Khai Nguyen, An T. Le +4
Robot learning in high-dimensional control settings, such as humanoid locomotion, presents persistent challenges for reinforcement learning (RL) algorithms due to unstable dynamics…
Model Tensor Planning
An T. Le, Khai Nguyen, Minh Nhat Vu +2
Sampling-based model predictive control (MPC) offers strong performance in nonlinear and contact-rich robotic tasks, yet often suffers from poor exploration due to locally greedy s…
FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching
Khang Nguyen, An T. Le, Tien Pham +3
Prior flow matching methods in robotics have primarily learned velocity fields to morph one distribution of trajectories into another. In this work, we extend flow matching to capt…
Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models
J. Carvalho, A. Le, P. Kicki +2
The performance of optimization-based robot motion planning algorithms is highly dependent on the initial solutions, commonly obtained by running a sampling-based planner to obtain…