28 citations · 92 across the 41 of their papers we have counts for
42 papers
ContactDP: Contact-Guided Diffusion Policy for Tight Insertion Tasks
Chengyi Xing, Shaoxiong Yao, Diego Romeres +1
High-precision connector insertion remains challenging for robotic systems due to tight mechanical tolerances, partial observability during contact, and multimodal uncertainty aris…
PPGuide: Steering Diffusion Policies with Performance Predictive Guidance
Zixing Wang, Devesh K. Jha, Ahmed H. Qureshi +1
Diffusion policies have shown to be very efficient at learning complex, multi-modal behaviors for robotic manipulation. However, errors in generated action sequences can compound o…
Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations
Yuki Shirai, Kei Ota, Devesh K. Jha +1
Non-prehensile manipulation is challenging due to complex contact interactions between objects, the environment, and robots. Model-based approaches can efficiently generate complex…
Learning global control of underactuated systems with Model-Based Reinforcement Learning
Niccolò Turcato, Marco Calì, Alberto Dalla Libera +3
This short paper describes our proposed solution for the third edition of the "AI Olympics with RealAIGym" competition, held at ICRA 2025. We employed Monte-Carlo Probabilistic Inf…
Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition
Felix Wiebe, Niccolò Turcato, Alberto Dalla Libera +17
In the field of robotics many different approaches ranging from classical planning over optimal control to reinforcement learning (RL) are developed and borrowed from other fields…
User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization
Joshua Hang Sai Ip, Ankush Chakrabarty, Ali Mesbah +1
Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the optimization procedure. Preferences are often abstracted in the f…
FDPP: Fine-tune Diffusion Policy with Human Preference
Yuxin Chen, Devesh K. Jha, Masayoshi Tomizuka +1
Imitation learning from human demonstrations enables robots to perform complex manipulation tasks and has recently witnessed huge success. However, these techniques often struggle…
Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning
Prasanth Sengadu Suresh, Siddarth Jain, Prashant Doshi +1
The growing interest in human-robot collaboration (HRC), where humans and robots cooperate towards shared goals, has seen significant advancements over the past decade. While previ…
RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
Shivam Vats, Devesh K. Jha, Maxim Likhachev +2
Model-based planners and controllers are commonly used to solve complex manipulation problems as they can efficiently optimize diverse objectives and generalize to long horizon tas…
Learning control of underactuated double pendulum with Model-Based Reinforcement Learning
Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo +2
This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm…
DECAF: a Discrete-Event based Collaborative Human-Robot Framework for Furniture Assembly
Giulio Giacomuzzo, Matteo Terreran, Siddarth Jain +1
This paper proposes a task planning framework for collaborative Human-Robot scenarios, specifically focused on assembling complex systems such as furniture. The human is characteri…
Autonomous Robotic Assembly: From Part Singulation to Precise Assembly
Kei Ota, Devesh K. Jha, Siddarth Jain +5
Imagine a robot that can assemble a functional product from the individual parts presented in any configuration to the robot. Designing such a robotic system is a complex problem w…
Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection
Xinghao Zhu, Devesh K. Jha, Diego Romeres +3
Automating the assembly of objects from their parts is a complex problem with innumerable applications in manufacturing, maintenance, and recycling. Unlike existing research, which…
Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks
Lingfeng Sun, Devesh K. Jha, Chiori Hori +5
Designing robotic agents to perform open vocabulary tasks has been the long-standing goal in robotics and AI. Recently, Large Language Models (LLMs) have achieved impressive result…
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
Giulio Giacomuzzos, Ruggero Carli, Diego Romeres +1
Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system…
Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning
Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli +2
In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components…
Style-transfer based Speech and Audio-visual Scene Understanding for Robot Action Sequence Acquisition from Videos
Chiori Hori, Puyuan Peng, David Harwath +7
To realize human-robot collaboration, robots need to execute actions for new tasks according to human instructions given finite prior knowledge. Human experts can share their knowl…
Learning Generalizable Pivoting Skills
Xiang Zhang, Siddarth Jain, Baichuan Huang +2
The skill of pivoting an object with a robotic system is challenging for the external forces that act on the system, mainly given by contact interaction. The complexity increases w…
Simultaneous Tactile Estimation and Control of Extrinsic Contact
Sangwoon Kim, Devesh K. Jha, Diego Romeres +2
We propose a method that simultaneously estimates and controls extrinsic contact with tactile feedback. The method enables challenging manipulation tasks that require controlling l…
Learning Control from Raw Position Measurements
Fabio Amadio, Alberto Dalla Libera, Daniel Nikovski +2
We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly…
Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control
Devesh K. Jha, Siddarth Jain, Diego Romeres +2
Robots have been steadily increasing their presence in our daily lives, where they can work along with humans to provide assistance in various tasks on industry floors, in offices,…
Active Exploration for Robotic Manipulation
Tim Schneider, Boris Belousov, Georgia Chalvatzaki +3
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years. One of the key challenges in manipulation i…
Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills
Seiji Shaw, Devesh K. Jha, Arvind Raghunathan +4
Dynamic movement primitives are widely used for learning skills which can be demonstrated to a robot by a skilled human or controller. While their generalization capabilities and s…
Design of Adaptive Compliance Controllers for Safe Robotic Assembly
Devesh K. Jha, Diego Romeres, Siddarth Jain +2
Insertion operations are a critical element of most robotic assembly operation, and peg-in-hole (PiH) insertion is one of the most widely studied tasks in the industrial and academ…