19 citations · 24 across the 28 of their papers we have counts for
28 papers
Potential-Field Action Representation for Reinforcement Learning in Contact-Rich Manipulation
Xinyu Liu, Gökhan Solak, Arash Ajoudani
Model-free reinforcement learning can acquire contact-rich robotic manipulation skills through trial-and-error interaction, but it often requires the policy to learn both task stra…
FORTE: Task-Adaptive Force Capability Optimization for Mobile Manipulators
Xiao Wang, Heng Zhang, Gokhan Solak +2
Effective physical interaction control in robotic manipulation requires not only kinematically feasible motion but also sufficient force-interaction capability. Existing redundancy…
How to Learn from What a Human Would Avoid? Intervention-Aware World Models with Real-World RL for Dexterous Manipulation
Jiaju Yin, Zhenhui Zhang, Lixin Xu +5
Multi-fingered dexterous manipulation remains a frontier for real-world reinforcement learning (RL) due to the high-dimensional action space and the prohibitive cost of hardware fa…
Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decomposition
Changhao Li, Yifang Zhang, Heng Zhang +6
Real-world online reinforcement learning (RL) provides a promising approach for training robotic manipulation policies directly in the physical world, avoiding the sim-to-real gap…
LUCID: Latent-Skill Unified Control via Imagined Dynamics for Long-Horizon Humanoid Loco-Manipulation
Cheng Guo, Mingzhe Ni, Angelo Cangelosi +1
Long-horizon humanoid loco-manipulation requires composing versatile whole-body skills and reliable high-level decision making. Existing methods often coordinate pretrained skills…
Robots Need More than VLA and World Models
Elis Karcini, Faisal Mehrban, Quang Nguyen +6
Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA) models, and expect broader g…
Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems
Youyuan Long, Gokhan Solak, Arash Ajoudani
Accurate dynamical modeling is essential for simulation and control of embodied systems, yet first-principles models of electromechanical systems often fail to capture complex diss…
IMPACT: A Dataset for Multi-Granularity Human Procedural Action Understanding in Industrial Assembly
Di Wen, Zeyun Zhong, David Schneider +18
We introduce IMPACT, a synchronized five-view RGB-D dataset for deployment-oriented industrial procedural understanding, built around real assembly and disassembly of a commercial…
Foundation Models in Robotics: A Comprehensive Review of Methods, Models, Datasets, Challenges and Future Research Directions
Aggelos Psiris, Vasileios Argyriou, Evangelos K. Markakis +5
Over the recent years, the field of robotics has been undergoing a transformative paradigm shift from fixed, single-task, domain-specific solutions towards adaptive, multi-function…
Reward-Zero: Language Embedding Driven Implicit Reward Mechanisms for Reinforcement Learning
Heng Zhang, Haddy Alchaer, Arash Ajoudani +1
We introduce Reward-Zero, a general-purpose implicit reward mechanism that transforms natural-language task descriptions into dense, semantically grounded progress signals for rein…
TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation
Kaidi Zhang, Heng Zhang, Zhengtong Xu +7
Vision-Language-Action (VLA) models have demonstrated significant advantages in robotic manipulation. However, their reliance on vision and language often leads to suboptimal perfo…
Self-supervised Physics-Informed Manipulation of Deformable Linear Objects with Non-negligible Dynamics
Youyuan Long, Gokhan Solak, Sara Zeynalpour +2
We address dynamic manipulation of deformable linear objects by presenting SPiD, a physics-informed self-supervised learning framework that couples an accurate deformable object mo…
PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation
Pengyuan Guo, Zhonghao Mai, Zhengtong Xu +8
Recent advances in vision-language models (VLMs) have enabled increasing progress in real-world robot manipulation. However, long-horizon manipulation in unstructured environments…
Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
Heng Zhang, Rui Dai, Gokhan Solak +4
Contact-rich tasks pose significant challenges for robotic systems due to inherent uncertainty, complex dynamics, and the high risk of damage during interaction. Recent advances in…
CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation
Heng Zhang, Wei-Hsing Huang, Qiyi Tong +7
We propose a CompliantVLA-adaptor that augments the state-of-the-art Vision-Language-Action (VLA) models with vision-language model (VLM)-informed context-aware variable impedance…
OmniVIC: A Self-Improving Variable Impedance Controller with Vision-Language In-Context Learning for Safe Robotic Manipulation
Heng Zhang, Wei-Hsing Huang, Gokhan Solak +1
We present OmniVIC, a universal variable impedance controller (VIC) enhanced by a vision language model (VLM), which improves safety and adaptation in any contact-rich robotic mani…
Imitation-Guided Bimanual Planning for Stable Manipulation under Changing External Forces
Kuanqi Cai, Chunfeng Wang, Zeqi Li +5
Robotic manipulation in dynamic environments often requires seamless transitions between different grasp types to maintain stability and efficiency. However, achieving smooth and a…
ActivePose: Active 6D Object Pose Estimation and Tracking for Robotic Manipulation
Sheng Liu, Zhe Li, Weiheng Wang +6
Accurate 6-DoF object pose estimation and tracking are critical for reliable robotic manipulation. However, zero-shot methods often fail under viewpoint-induced ambiguities and fix…
Online Learning for Vibration Suppression in Physical Robot Interaction using Power Tools
Gokhan Solak, Arash Ajoudani
Vibration suppression is an important capability for collaborative robots deployed in challenging environments such as construction sites. We study the active suppression of vibrat…
A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
Toshiaki Tsuji, Yasuhiro Kato, Gokhan Solak +4
This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the en…
Context-aware collaborative pushing of heavy objects using skeleton-based intention prediction
Gokhan Solak, Gustavo J. G. Lahr, Idil Ozdamar +1
In physical human-robot interaction, force feedback has been the most common sensing modality to convey the human intention to the robot. It is widely used in admittance control to…
Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets
Gustavo J. G. Lahr, Ricardo V. Godoy, Thiago H. Segreto +4
Automating aircraft manufacturing still relies heavily on human labor due to the complexity of the assembly processes and customization requirements. One key challenge is achieving…
Scaling Laws in Scientific Discovery with AI and Robot Scientists
Pengsong Zhang, Heng Zhang, Huazhe Xu +7
Scientific discovery is poised for rapid advancement through advanced robotics and artificial intelligence. Current scientific practices face substantial limitations as manual expe…
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Heng Zhang, Gokhan Solak, Arash Ajoudani
Ensuring safety in reinforcement learning (RL)-based robotic systems is a critical challenge, especially in contact-rich tasks within unstructured environments. While the state-of-…