8 citations · 16 across the 9 of their papers we have counts for
10 papers
TWISTED-RL: Hierarchical Skilled Agents for Knot-Tying without Human Demonstrations
Guy Freund, Tom Jurgenson, Matan Sudry +1
Robotic knot-tying represents a fundamental challenge in robotics due to the complex interactions between deformable objects and strict topological constraints. We present TWISTED-…
VLM-Guided Experience Replay
Elad Sharony, Tom Jurgenson, Orr Krupnik +2
Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) have enabled powerful semantic and multimodal reasoning capabilities, creating new opportunities t…
Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees
Yaniv Hassidof, Tom Jurgenson, Kiril Solovey
Kinodynamic motion planning is concerned with computing collision-free trajectories while abiding by the robot's dynamic constraints. This critical problem is often tackled using s…
RoboArm-NMP: a Learning Environment for Neural Motion Planning
Tom Jurgenson, Matan Sudry, Gal Avineri +1
We present RoboArm-NMP, a learning and evaluation environment that allows simple and thorough evaluations of Neural Motion Planning (NMP) algorithms, focused on robotic manipulator…
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
Zohar Rimon, Tom Jurgenson, Orr Krupnik +2
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by lo…
Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
Orr Krupnik, Elisei Shafer, Tom Jurgenson +1
Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian infere…
Goal-Conditioned Supervised Learning with Sub-Goal Prediction
Tom Jurgenson, Aviv Tamar
Recently, a simple yet effective algorithm -- goal-conditioned supervised-learning (GCSL) -- was proposed to tackle goal-conditioned reinforcement-learning. GCSL is based on the pr…
Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning
Tom Jurgenson, Or Avner, Edward Groshev +1
Many AI problems, in robotics and other domains, are goal-based, essentially seeking trajectories leading to various goal states. Reinforcement learning (RL), building on Bellman's…
Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization
Tom Jurgenson, Edward Groshev, Aviv Tamar
Many AI problems, in robotics and other domains, are goal-directed, essentially seeking a trajectory leading to some goal state. In such problems, the way we choose to represent a…
Harnessing Reinforcement Learning for Neural Motion Planning
Tom Jurgenson, Aviv Tamar
Motion planning is an essential component in most of today's robotic applications. In this work, we consider the learning setting, where a set of solved motion planning problems is…