68 citations · 196 across the 49 of their papers we have counts for
53 papers
Tactile Curiosity Drives Robot Interaction
Klemens Iten, Alexander Proshkin, Bhavya Sukhija +4
Mastering robot manipulation skills via reinforcement learning (RL) remains largely sample-inefficient. The most common RL algorithms rely on random action sampling to discover new…
Two2Four: Generative Quadruped Puppeteering from Human Motion
Fatemeh Zargarbashi, Zehong Qiu, Dhruv Agrawal +4
Realistic animal motion for virtual production is typically obtained either through motion capture of highly trained performers who accurately mimic animal behavior, or by retarget…
PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots
Zhihao Cao, Tianxu An, Chenhao Li +2
Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation. This becomes particularly challenging in co…
Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning
Jiajun Hu, Nuria Armengol Urpi, Jin Cheng +1
Zero-shot reinforcement learning (RL) algorithms aim to learn a family of policies from a reward-free dataset, and recover optimal policies for any reward function directly at test…
VQ-Style: Disentangling Style and Content in Motion with Residual Quantized Representations
Fatemeh Zargarbashi, Dhruv Agrawal, Jakob Buhmann +3
Human motion data is inherently rich and complex, containing both semantic content and subtle stylistic features that are challenging to model. We propose a novel method for effect…
What Matters for Simulation to Online Reinforcement Learning on Real Robots
Yarden As, Dhruva Tirumala, René Zurbrügg +4
We investigate what specific design choices enable successful online reinforcement learning (RL) on physical robots. Across 100 real-world training runs on three distinct robotic p…
Safe Exploration via Policy Priors
Manuel Wendl, Yarden As, Manish Prajapat +3
Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.g. simulated) environments. In this work, we tackle thi…
TARC: Time-Adaptive Robotic Control
Arnav Sukhija, Lenart Treven, Jin Cheng +3
Most robotic systems rely on fixed-frequency discrete-time controllers, creating a trade-off between the efficiency of low-frequency control and the responsiveness of high-frequenc…
SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
Yarden As, Chengrui Qu, Benjamin Unger +6
Deploying reinforcement learning (RL) safely in the real world is challenging, as policies trained in simulators must face the inevitable sim-to-real gap. Robust safe RL techniques…
Collaborative Loco-Manipulation for Pick-and-Place Tasks with Dynamic Reward Curriculum
Tianxu An, Flavio De Vincenti, Yuntao Ma +2
We present a hierarchical RL pipeline for training one-armed legged robots to perform pick-and-place (P&P) tasks end-to-end -- from approaching the payload to releasing it at a tar…
Simulation Priors for Data-Efficient Deep Learning
Lenart Treven, Bhavya Sukhija, Jonas Rothfuss +3
How do we enable AI systems to efficiently learn in the real-world? First-principles models are widely used to simulate natural systems, but often fail to capture real-world comple…
Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids
Arturo Flores Alvarez, Fatemeh Zargarbashi, Havel Liu +7
We present a Reinforcement Learning (RL)-based locomotion system for Cosmo, a custom-built humanoid robot designed for entertainment applications. Unlike traditional humanoids, ent…
CAIMAN: Causal Action Influence Detection for Sample-efficient Loco-manipulation
Yuanchen Yuan, Jin Cheng, Núria Armengol Urpí +1
Enabling legged robots to perform non-prehensile loco-manipulation is crucial for enhancing their versatility. Learning behaviors such as whole-body object pushing often requires s…
Learning More With Less: Sample Efficient Model-Based RL for Loco-Manipulation
Benjamin Hoffman, Jin Cheng, Chenhao Li +1
By combining the agility of legged locomotion with the capabilities of manipulation, loco-manipulation platforms have the potential to perform complex tasks in real-world applicati…
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
Bhavya Sukhija, Stelian Coros, Andreas Krause +2
Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms u…
Budget-optimal multi-robot layout design for box sorting
Peiyu Zeng, Yijiang Huang, Simon Huber +1
Robotic systems are routinely used in the logistics industry to enhance operational efficiency, but the design of robot workspaces remains a complex and manual task, which limits t…
Problem Space Transformations for Out-of-Distribution Generalisation in Behavioural Cloning
Kiran Doshi, Marco Bagatella, Stelian Coros
The combination of behavioural cloning and neural networks has driven significant progress in robotic manipulation. As these algorithms may require a large number of demonstrations…
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
Yarden As, Bhavya Sukhija, Lenart Treven +3
Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with…
PokeFlex: Towards a Real-World Dataset of Deformable Objects for Robotic Manipulation
Jan Obrist, Miguel Zamora, Hehui Zheng +3
Advancing robotic manipulation of deformable objects can enable automation of repetitive tasks across multiple industries, from food processing to textiles and healthcare. Yet robo…
MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation
Fatemeh Zargarbashi, Fabrizio Di Giuro, Jin Cheng +3
This work presents a meta-reinforcement learning approach to develop a universal locomotion control policy capable of zero-shot generalization across diverse quadrupedal platforms.…
RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
Fatemeh Zargarbashi, Jin Cheng, Dongho Kang +2
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level obj…
NeoRL: Efficient Exploration for Nonepisodic RL
Bhavya Sukhija, Lenart Treven, Florian Dörfler +2
We study the problem of nonepisodic reinforcement learning (RL) for nonlinear dynamical systems, where the system dynamics are unknown and the RL agent has to learn from a single t…
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers
Fan Shi, Chong Zhang, Takahiro Miki +3
Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neur…
Spatio-Temporal Motion Retargeting for Quadruped Robots
Taerim Yoon, Dongho Kang, Seungmin Kim +4
This work presents a motion retargeting approach for legged robots, aimed at transferring the dynamic and agile movements to robots from source motions. In particular, we guide the…