#icra2024

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

We trained ANYmal to go into confined spaces such as under collapsed buildings. To be presented at #ICRA2024 Title: Learning to walk in confined spaces using 3D representation Arxiv: Video: Summary Page:

We trained ANYmal to go into confined spaces such as under collapsed buildings. To be presented at #ICRA2024 Title: Learning to walk in confined spaces using 3D representation Arxiv: Video: Summary Page:

72,185 次观看 • 2 年前

#ICRA2024 ではもう一件，6自由度モンテカルロ自己位置推定の発表をします．IMU無し・初期値無しで完全な誘拐状態からの復帰を実現しました．自信作です！ MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter

#ICRA2024 ではもう一件，6自由度モンテカルロ自己位置推定の発表をします．IMU無し・初期値無しで完全な誘拐状態からの復帰を実現しました．自信作です！ MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter

42,731 次观看 • 2 年前

Check out our #ICRA2024 paper "Actor-Critic Model Predictive Control." Model-free #reinforcementlearning (RL) is known for its strong task performance and flexibility in optimizing general reward formulations. On the other hand, #ModelPredictiveControl (MPC) benefits from robustness and online replanning capabilities. We combine both approaches by introducing a new framework called Actor-Critic Model Predictive Control. The key idea is to embed a differentiable MPC within an Actor-Critic RL framework. The proposed approach leverages the short-term predictive optimization capabilities of MPC with the exploratory and end-to-end training properties of RL. The resulting policy effectively manages both short-term decisions through the MPC-based actor and long-term prediction via the critic network, unifying the benefits of both model-based control and end-to-end learning. We validate our method in simulation and the real world with a quadcopter across various high-level tasks. We show that the proposed architecture can achieve real-time control performance, learn complex behaviors via trial and error, and retain the predictive properties of the MPC to better handle out-of-distribution behavior. Paper: Full Video with more details: Kudos to Ángel Romero, Yunlong Song IEEE ICRA University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)

Check out our #ICRA2024 paper "Actor-Critic Model Predictive Control." Model-free #reinforcementlearning (RL) is known for its strong task performance and flexibility in optimizing general reward formulations. On the other hand, #ModelPredictiveControl (MPC) benefits from robustness and online replanning capabilities. We combine both approaches by introducing a new framework called Actor-Critic Model Predictive Control. The key idea is to embed a differentiable MPC within an Actor-Critic RL framework. The proposed approach leverages the short-term predictive optimization capabilities of MPC with the exploratory and end-to-end training properties of RL. The resulting policy effectively manages both short-term decisions through the MPC-based actor and long-term prediction via the critic network, unifying the benefits of both model-based control and end-to-end learning. We validate our method in simulation and the real world with a quadcopter across various high-level tasks. We show that the proposed architecture can achieve real-time control performance, learn complex behaviors via trial and error, and retain the predictive properties of the MPC to better handle out-of-distribution behavior. Paper: Full Video with more details: Kudos to Ángel Romero, Yunlong Song IEEE ICRA University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)

Davide Scaramuzza

34,874 次观看 • 2 年前

A long version video of the Food Topping Challenge. #ICRA2024

A long version video of the Food Topping Challenge. #ICRA2024

Sho Sakaino (境野翔)

23,955 次观看 • 2 年前

Check out our #ICRA2024 paper "Contrastive Initial State Buffer for Reinforcement Learning," which tackles the sample inefficiency in #ReinforcementLearning head-on. Code released! We introduce an approach agnostic to the underlying RL algorithm: the Contrastive Initial State Buffer. This tool strategically selects states from past experiences and uses them to initialize the agent in the environment to guide it toward more informative states. Our experiments on drone racing and legged locomotion show that our method achieves higher task performance while also speeding up training convergence. Reference: Nico Messikommer, Yunlong Song, Davide Scaramuzza Contrastive Initial State Buffer for Reinforcement Learning IEEE International Conference on Robotics and Automation (ICRA), 2024. PDF: Code: Video: Kudos to Messikommer Yunlong Song Aerial Core European Research Council (ERC) University of Zurich UZH Space Hub IEEE ICRA UZH Science

Check out our #ICRA2024 paper "Contrastive Initial State Buffer for Reinforcement Learning," which tackles the sample inefficiency in #ReinforcementLearning head-on. Code released! We introduce an approach agnostic to the underlying RL algorithm: the Contrastive Initial State Buffer. This tool strategically selects states from past experiences and uses them to initialize the agent in the environment to guide it toward more informative states. Our experiments on drone racing and legged locomotion show that our method achieves higher task performance while also speeding up training convergence. Reference: Nico Messikommer, Yunlong Song, Davide Scaramuzza Contrastive Initial State Buffer for Reinforcement Learning IEEE International Conference on Robotics and Automation (ICRA), 2024. PDF: Code: Video: Kudos to Messikommer Yunlong Song Aerial Core European Research Council (ERC) University of Zurich UZH Space Hub IEEE ICRA UZH Science

Davide Scaramuzza

13,846 次观看 • 2 年前

Unfortunately, I am not at #ICRA2024, but students from LeCAR Lab at CMU will present several papers about learning and control in agile robotics. Check them out!

Unfortunately, I am not at #ICRA2024, but students from LeCAR Lab at CMU will present several papers about learning and control in agile robotics. Check them out!

10,087 次观看 • 2 年前

没有更多内容可加载