193 citations · 386 across the 51 of their papers we have counts for
62 papers
Magnet-Aware Control of Legged Robots
J. Playan Garai, S. B. Djuve, C. McGreavy +1
Autonomous robots can increase uptime and reduce human exposure in Big Science facilities, but strong magnetic fields needed for their operation corrupt sensors and induce pose-dep…
FERPO: Forward Entropy-Regularized Policy Optimization
Sebastian Sanokowski, Alireza Sarmadi, Majid Khadiv
Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic. However, critics are typically…
Res-HIL: Human-Guided Residual Reinforcement Learning for Sample-Efficient Dexterous Manipulation
Mariia Iavorskaia, Christian Dietz, Sebastian Albrecht +1
Imitation learning enables robots to acquire manipulation skills from demonstrations, but the resulting policies can fail outside the training data, while collecting more demonstra…
Adaptive-MHE : A Sampling-Based Adaptive MPC for Legged Loco-Manipulation via Moving Horizon Estimation
Hossein Keshavarz, Alejandro Ramirez-Serrano, Majid Khadiv
Legged robots have demonstrated a remarkable ability to traverse various terrains, yet generating effective loco-manipulation behaviors remains challenging. A key difficulty is tha…
Probabilistic Reachable-Action Verification of Visuomotor Policies via Set-Based Training
Yanliang Huang, Zhuocheng Zhang, Peng Xie +5
Reachability analysis for visuomotor policies is difficult because large visual encoders make end-to-end set propagation computationally expensive and excessively conservative. We…
PAC-DP: PAC-Bayesian Diffusion Policy Learning
Mohammad Hasan Yeganegi, Dian Yu, Andrea Del Prete +2
Diffusion Policies (DPs) are able to perform complex manipulation tasks. However, DPs are typically trained by minimizing a denoising objective, which provides limited control over…
FARO: Feasibility-Aware Robot Motion Optimization
Michal Ciebielski, Shafeef Omar, Aaron Johnson +1
Fast planning of novel behaviors in unseen scenarios remains a fundamental challenge in robotics. The high-dimensional, hybrid, and underactuated nature of humanoid loco-manipulati…
SafeExplorer: An Unbiased Mixed-Policy Gradient for Reinforcement Learning with Recovery Interventions
Elham Daneshmand, Majid Khadiv, Glen Berseth +1
Training agents directly on the task they will be deployed on often leads to the best policy performance, but training on physical robots can cause damage through falls, so minimiz…
Humanoid-DART: Humanoid Loco-Manipulation using Diffusion-guided Augmentation through Relabeling and Tracking
Pranav Debbad, Kanish Thiagarajan, Victor Dhédin +2
Imitating human demonstrations has emerged as a dominant paradigm for learning humanoid loco-manipulation policies. However, scaling these approaches remains challenging due to the…
Guided Discovery of New Behaviors using Diffusion Policies
Dian Yu, Sebastian Sanokowski, Majid Khadiv
Diffusion models have become a powerful tool for generative modeling in robotics, with diffusion policies excelling at modeling multimodal action-trajectory distributions. However,…
Shield-Loco: Shielding Locomotion Policies with Predictive Safety Filtering
Aditya Shirwatkar, Sebastian Sanokowski, Shishir Kolathaya +2
Reinforcement learning (RL) policies enable dynamic legged locomotion but lack mechanisms to avoid violations of safety constraints that are absent during training. Large-scale off…
MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation
Ilyass Taouil, Michal Ciebelski, Shafeef Omar +4
We present MotionDisco, a framework that discovers contact-rich, long-horizon humanoid loco-manipulation motions from scratch, without relying on teleoperation or motion retargetin…
Convergence of Consensus-Based Particle Methods for Nonconvex Bi-Level Optimization
Yutong Chao, Xudong Sun, Konstantin Riedl +2
In this paper, we study a consensus-based optimization method for nonconvex bi-level optimization, where the objective is to minimize an upper-level function over the set of global…
SLowRL: Safe Low-Rank Adaptation Reinforcement Learning for Locomotion
Elham Daneshmand, Shafeef Omar, Glen Berseth +2
Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tuning these policies directly on hardware is…
Last-iterate Convergence of ADMM on Multi-affine Quadratic Equality Constrained Problem
Yutong Chao, Michal Ciebielski, Jalal Etesami +1
In this paper, we study a class of non-convex optimization problems known as multi-affine quadratic equality constrained problems, which appear in various applications--from genera…
Safe-Night VLA: Seeing the Unseen via Thermal-Perceptive Vision-Language-Action Models for Safety-Critical Manipulation
Dian Yu, Qingchuan Zhou, Bingkun Huang +2
Current Vision-Language-Action (VLA) models rely primarily on RGB perception, preventing them from capturing modalities such as thermal signals that are imperceptible to convention…
Hippo: High-performance Interior-Point and Projection-based Solver for Generic Constrained Trajectory Optimization
Haizhou Zhao, Ludovic Righetti, Majid Khadiv
Trajectory optimization is the core of modern model-based robotic control and motion planning. Existing trajectory optimizers, based on sequential quadratic programming (SQP) or di…
Consensus-based optimization (CBO): Towards Global Optimality in Robotics
Xudong Sun, Armand Jordana, Massimo Fornasier +2
Zero-order optimization has recently received significant attention for designing optimal trajectories and policies for robotic systems. However, most existing methods (e.g., MPPI,…
SURE: Safe Uncertainty-Aware Robot-Environment Interaction using Trajectory Optimization
Zhuocheng Zhang, Haizhou Zhao, Xudong Sun +2
Robotic tasks involving contact interactions pose significant challenges for trajectory optimization due to discontinuous dynamics. Conventional formulations typically assume deter…
DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization
Victor Dhedin, Ilyass Taouil, Shafeef Omar +4
In this paper, we introduce DynaRetarget, a complete pipeline for retargeting human motions to humanoid control policies. The core component of DynaRetarget is a novel Sampling-Bas…
Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning
Shafeef Omar, Majid Khadiv
We present a unified framework for multi-task locomotion and manipulation policy learning grounded in a contact-explicit representation. Instead of designing different policies for…
Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion
Victor Dhédin, Haizhou Zhao, Majid Khadiv
Legged robots have the potential to traverse highly constrained environments with agile maneuvers. However, planning such motions requires solving a highly challenging optimization…
Task and Motion Planning for Humanoid Loco-manipulation
Michal Ciebielski, Victor Dhédin, Majid Khadiv
This work presents an optimization-based task and motion planning (TAMP) framework that unifies planning for locomotion and manipulation through a shared representation of contact…
Control of Legged Robots using Model Predictive Optimized Path Integral
Hossein Keshavarz, Alejandro Ramirez-Serrano, Majid Khadiv
Legged robots possess a unique ability to traverse rough terrains and navigate cluttered environments, making them well-suited for complex, real-world unstructured scenarios. Howev…