Robotic Systems Lab

We’re delighted to announce that our paper MARLadona – Towards Cooperative Team Play Using Multi-Agent Reinforcement Learning has been accepted for presentation at ICRA 2025 in Atlanta! This milestone is a testament to the hard work and dedication of our team. If you missed our earlier updates, check out our short YouTube video for a brief overview of the work behind the scenes. Video: https://lnkd.in/d95SerYc Paper: https://lnkd.in/dDqdmUst Thank you for your ongoing support and interest in our research! #ICRA2025 #Robotics #MultiAgentRL #AI #Research

https://lnkd.in/dVCBXgNy

We introduce Deep Fourier Mimic, a generalized version of DeepMimic, which enables automatic parameterization of reference motions. This means you can learn diverse motions with a single policy conditioned on their meaningful spatial and temporal representations! Paper: https://lnkd.in/eFkxaK_C Project site: https://lnkd.in/ecT7W5dk Video: https://lnkd.in/eF95xqdh Authors: Ryo Watanabe, Chenhao Li, Marco Hutter Sony, ETH AI Center, Robotic Systems Lab #ai #aibo #sony #robotics #robotdogs #reinforcementlearning #imitationlearning #learningfromdemonstrations #animal #motions #computergraphics

Check out our #ICRA2025 paper, where we train a robotic puppy to dance expressively! Our method, Deep Fourier Mimic, a generalized version of DeepMimic, enables automatic parameterization of reference data. Paper: https://lnkd.in/eFkxaK_C Project site: https://lnkd.in/ecT7W5dk Video: https://lnkd.in/eF95xqdh Traditionally, dancing motions have been manually designed by artists, a process that is both labor-intensive and restricted to simple motion playback, lacking the flexibility to incorporate additional tasks such as locomotion or gaze control during dancing. To overcome these challenges, we propose Deep Fourier Mimic (DFM), a novel method that combines advanced motion representation with Reinforcement Learning to enable smooth transitions between motions while concurrently managing auxiliary tasks during dance sequences. The expressive dance motion learning system is composed of four key components: motion design, motion representation, motion learning, and hardware inference. In the motion design phase, artists create motion references using specialized design software. The representation of these diverse motions is then learned using a Periodic Autoencoder. Reinforcement learning is employed to enable the robot to perform auxiliary tasks, such as walking and head orientation control, while accurately tracking the designed dance references. The tracking accuracy of DFM is demonstrated by conditioning the reference dance to a rear leg lifting motion. We compare our method with Fourier Latent Dynamics (FLD) as a baseline. Due to strong periodic assumptions in both motion representation and reinforcement learning, FLD overly smooths out reference motions. DFM, which relaxes the strong periodic assumption, results in moving up the rear leg by tracking the reference motion details more accurately. DeepMimic yields high tracking performance on single trajectories but lacks the capabilities to deal with diverse motions. The resulting hard switches lead to jerky changes of motion types. In contrast, the motion representation employed by DFM achieves smooth transitions. The modulation from higher to slower frequency by conditioning the mainly head-moving dance motion is shown. Even though the training dataset consists of discrete frequency types, the motion representation allows for continuous frequency interpolation. Authors: Ryo Watanabe, Chenhao Li, Marco Hutter #ai #aibo #sony #robotics #robotdogs #reinforcementlearning #imitationlearning #learningfromdemonstrations #animal #motions #computergraphics

We are happy to share that our work "Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning" has been accepted for publication at #ICRA2025 ! We developed a constrained reinforcement learning-based controller for pushing an unknown object to a desired planar position and yaw orientation with a quadrupedal mobile manipulator. Our approach enables contact-rich nonprehensile interactions, achieves robustness against unknown objects, and keeps the object balanced even on high-friction floorings. This work is a joint collaboration with the HHCM lab of Istituto Italiano di Tecnologia.  📺 Video: https://lnkd.in/ezbDsRy2  📜 Paper: https://lnkd.in/e8ai_2Kf  👨💻 Authors: Ioannis Dadiotis, Mayank M., Nikos Tsagarakis, Marco Hutter

Big News: Swiss-Mile is now RIVR! We’re thrilled to unveil our new identity as RIVR, reflecting our evolution from a university spin-off to a global leader in Physical AI and robotics. In 2025, we’ll be deploying our groundbreaking wheeled-legged robots with major logistics carriers for last-mile delivery to set new standards for efficiency and sustainability. Our focus? Revolutionize delivery, giving 1 human the power of 1000. This milestone follows our $22M seed round co-led by Jeff Bezos through Bezos Expeditions and HSG. Now, we’re gearing up for even more growth, aiming to transform one of the most critical aspects of e-commerce logistics. The name RIVR symbolizes connectivity, adaptability, and flow, mirroring our mission to Reinvent Robotics for autonomous logistics. #PhysicalAI #Robotics #LastMileDelivery #Sustainability

Santa forgot a very special present this year, but luckily someone was there to step in and save the day!🎅🤖 Happy holidays from all of us at RSL! 🎄✨ 👉 https://lnkd.in/d_-JSjtY

Our legged robots cannot yet run at 55 km/h - However, the traversability estimation and mapping are ready. In collaboration with NASA Jet Propulsion Laboratory we present our latest work accepted in RA-L 2024 : RoadRunner M&M: Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation  Arxiv: https://lnkd.in/dCECXrSN YouTube: https://lnkd.in/dqWWzAr3 Webpage: https://lnkd.in/dM3qer3z Authors: Manthan Patel, Jonas Frey, Deegan Atha, Patrick Spieler, Marco Hutter, Shehryar Khattak This work is a major improvement on our previous collaboration, RoadRunner, and was led by Manthan Patel as part of his Master`s Thesis. In addition, we are happy to announce that Manthan Patel joined us at the Robotic Systems Lab.

Simulation is a key enabler for embodied AI. It allows us to massively scale up data collection in order to experience years in minutes. NVIDIA's Isaac Lab facilitates easy integration of our robot models into Isaac Sim for rapid learning of tasks which directly transfer to the real world. This way, we can teach the robot new skills in just a few days. If you want to help spearhead the future of robotics and embodied AI, join us: https://lnkd.in/eZ36eRNW NVIDIA Robotics #robotics #ai #nvidia

🌟 Excited to Share Our Latest Work on Reinforcement Learning for Multi-Contact Loco-Manipulation! 🌟 In the field of Reinforcement Learning (RL), designing the right Markov Decision Process (MDP) for each task can be challenging and time-consuming. Our work tackles this issue by proposing a systematic approach to behavior synthesis and control for complex, multi-contact loco-manipulation tasks. Using just a single demonstration per task, we train RL policies to track demonstrations robustly—without any task-specific tuning. We show that the learned policies can be transferred to hardware, achieving adaptive recovery behaviors like handle re-grasping. 📢 Catch our presentation! We’ll be presenting in Oral Session 2 (14:30 - 15:30 CET) today at the Conference on Robot Learning #CoRL2024. Looking forward to feedback and collaboration! 👥 Authors: Jean-Pierre Sleiman*, Mayank M.*, Marco Hutter 🔗 Check out the full paper: https://lnkd.in/gBrsa5QG