2 citations · 2 across the 14 of their papers we have counts for
14 papers · 1 filter
Geometry-aware Policy Imitation
Yiming Li, Nael Darwiche, Amirreza Razmjoo +4
We propose a Geometry-aware Policy Imitation (GPI) approach that rethinks imitation learning by treating demonstrations as geometric curves rather than collections of state-action…
Monte Carlo Tree Search with Tensor Factorization for Optimization Problems in Robotics
Teng Xue, Yan Zhang, Amirreza Razmjoo +1
Many robotic tasks, such as inverse kinematics, motion planning, and contact-rich manipulation, can be formulated as optimization problems. Solving these problems requires addressi…
A Smooth Analytical Formulation of Collision Detection and Rigid Body Dynamics With Contact
Onur Beker, Nico Gürtler, Ji Shi +4
Generating intelligent robot behavior in contact-rich settings is a research problem where zeroth-order methods currently prevail. A major contributor to the success of such method…
CCDP: Composition of Conditional Diffusion Policies with Guided Sampling
Amirreza Razmjoo, Sylvain Calinon, Michael Gienger +1
Imitation Learning offers a promising approach to learn directly from data without requiring explicit models, simulations, or detailed task definitions. During inference, actions a…
Sampling-Based Constrained Motion Planning with Products of Experts
Amirreza Razmjoo, Teng Xue, Suhan Shetty +1
We present a novel approach to enhance the performance of sampling-based Model Predictive Control (MPC) in constrained optimization by leveraging products of experts. Our methodolo…
Robust Contact-rich Manipulation through Implicit Motor Adaptation
Teng Xue, Amirreza Razmjoo, Suhan Shetty +1
Contact-rich manipulation plays an important role in daily human activities. However, uncertain physical parameters often pose significant challenges for both planning and control.…
Robust Manipulation Primitive Learning via Domain Contraction
Teng Xue, Amirreza Razmjoo, Suhan Shetty +1
Contact-rich manipulation plays an important role in human daily activities, but uncertain parameters pose significant challenges for robots to achieve comparable performance throu…
Learn2Decompose: Learning Problem Decomposition for Efficient Sequential Multi-object Manipulation Planning
Yan Zhang, Teng Xue, Amirreza Razmjoo +1
We present an efficient task and motion replanning approach for sequential multi-object manipulation in dynamic environments. Conventional Task And Motion Planning (TAMP) solvers e…
Configuration Space Distance Fields for Manipulation Planning
Yiming Li, Xuemin Chi, Amirreza Razmjoo +1
The signed distance field is a popular implicit shape representation in robotics, providing geometric information about objects and obstacles in a form that can easily be combined…
Logic-Skill Programming: An Optimization-based Approach to Sequential Skill Planning
Teng Xue, Amirreza Razmjoo, Suhan Shetty +1
Recent advances in robot skill learning have unlocked the potential to construct task-agnostic skill libraries, facilitating the seamless sequencing of multiple simple manipulation…
Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments
Yan Zhang, Teng Xue, Amirreza Razmjoo +1
Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitati…
Learning Joint Space Reference Manifold for Reliable Physical Assistance
Amirreza Razmjoo, Tilen Brecelj, Kristina Savevska +3
This paper presents a study on the use of the Talos humanoid robot for performing assistive sit-to-stand or stand-to-sit tasks. In such tasks, the human exerts a large amount of fo…
D-LGP: Dynamic Logic-Geometric Program for Reactive Task and Motion Planning
Teng Xue, Amirreza Razmjoo, Sylvain Calinon
Many real-world sequential manipulation tasks involve a combination of discrete symbolic search and continuous motion planning, collectively known as combined task and motion plann…
Representing Robot Geometry as Distance Fields: Applications to Whole-body Manipulation
Yiming Li, Yan Zhang, Amirreza Razmjoo +1
In this work, we propose a novel approach to represent robot geometry as distance fields (RDF) that extends the principle of signed distance fields (SDFs) to articulated kinematic…