21 citations · 27 across the 30 of their papers we have counts for
11 papers · 1 filter
Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning
Mariano Ramírez Montero, Ebrahim Shahabi, Giovanni Franzese +3
Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, bu…
Task-agnostic Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation
Pengzhi Yang, Xinyu Wang, Ruipeng Zhang +3
A fundamental objective in intelligent robotics is to move towards lifelong learning robot that can learn and adapt to unseen scenarios over time. However, continually learning new…
Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
Cong Wang, Aoming Liang, Fei Han +4
Reinforcement learning-based quadruped robots excel across various terrains but still lack the ability to swim in water due to the complex underwater environment. This paper presen…
ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input
Petr Vanc, Giovanni Franzese, Jan Kristof Behrens +4
Learning from demonstration is a promising approach for teaching robots new skills. However, a central challenge in the execution of acquired skills is the ability to recognize fau…
Scalable Task Planning via Large Language Models and Structured World Representations
Rodrigo Pérez-Dattari, Zhaoting Li, Robert Babuška +2
Planning methods struggle with computational intractability in solving task-level problems in large-scale environments. This work explores leveraging the commonsense knowledge enco…
EAGERx: Graph-Based Framework for Sim2real Robot Learning
Bas van der Heijden, Jelle Luijkx, Laura Ferranti +2
Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics due to its potential to efficiently handle com…
Generalizable Motion Policies through Keypoint Parameterization and Transportation Maps
Giovanni Franzese, Ravi Prakash, Cosimo Della Santina +1
Learning from Interactive Demonstrations has revolutionized the way non-expert humans teach robots. It is enough to kinesthetically move the robot around to teach pick-and-place, d…
ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Runyu Ma, Jelle Luijkx, Zlatan Ajanovic +1
In robot manipulation, Reinforcement Learning (RL) often suffers from low sample efficiency and uncertain convergence, especially in large observation and action spaces. Foundation…
RACP: Risk-Aware Contingency Planning with Multi-Modal Predictions
Khaled A. Mustafa, Daniel Jarne Ornia, Jens Kober +1
For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is imperative to assess the repercussions of its prospective actions by anticipating the uncer…
Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design
Vassil Atanassov, Jiatao Ding, Jens Kober +2
Deep reinforcement learning (DRL) has emerged as a promising solution to mastering explosive and versatile quadrupedal jumping skills. However, current DRL-based frameworks usually…
ROME: Robust Multi-Modal Density Estimator
Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora +2
The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demon…