From the 1 of 15 linked papers with an AI index.
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Pretraining in Actor-Critic Reinforcement Learning for Locomotion
Jiale Fan, Andrei Cramariuc, Tifanny Portela +1
The paper proposes a pretraining approach that learns a proprioceptive inverse dynamics model from task‑agnostic exploration data and uses its weights to warm‑start actor‑critic re…
Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing
Jiale Fan, Connor Flynn, Tianao Xu +4
Learning-based control has revolutionized dynamic locomotion, yet navigating unstructured terrain remains limited by a robot's incomplete awareness of imminent ground contact. Whil…
Learning Multi-Agent Local Collision-Avoidance for Collaborative Carrying tasks with Coupled Quadrupedal Robots
Francesca Bray, Simone Tolomei, Andrei Cramariuc +2
Robotic collaborative carrying could greatly benefit human activities like warehouse and construction site management. However, coordinating the simultaneous motion of multiple rob…
Beyond Cybathlon: On-demand Quadrupedal Assistance for People with Limited Mobility
Carmen Scheidemann, Andrei Cramariuc, Changan Chen +2
Background: Assistance robots have the potential to increase the independence of people who need daily care due to limited mobility or being wheelchair-bound. Current solutions of…
DexEvolve: Evolutionary Optimization for Robust and Diverse Dexterous Grasp Synthesis
René Zurbrügg, Andrei Cramariuc, Marco Hutter
Dexterous grasping is fundamental to robotics, yet data-driven grasp prediction heavily relies on large, diverse datasets that are costly to generate and typically limited to a nar…
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning
Ioannis Dadiotis, Mayank Mittal, Nikos Tsagarakis +1
Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object's physical properties and friction with the floo…