Deep Reinforcement Learning (RL) has become a popular method for controlling legged robots in various applications. Recent studies have showcased remarkable achievements in solving complex tasks like navigating through challenging environments and performing loco-manipulation. However, these advancements often entail intricate learning procedures that involve meticulous reward shaping and feature engineering to ensure successful convergence.
In a recent study, researchers addressed these challenges by successfully implementing loco-manipulation with a humanoid robot using an RL algorithm that incorporates constraints through stochastic terminations during policy learning. By minimizing the number of rewards and transforming them into constraints when feasible, the study aimed to simplify the learning process. Additionally, the research explored the impact of different learning features found in existing literature. The findings revealed that providing noise-free observations and avoiding privileged information for the critic can significantly enhance locomotion performance, especially on rugged terrains.
Furthermore, the study illustrated that the streamlined architecture proposed is not limited to basic locomotion but can also be extended to tasks involving both locomotion and manipulation, including upper limb movements. For those interested, videos of the experiments can be accessed at humanoid-cat.github.io.
For more information or inquiries related to the study, individuals can contact the contributor through the provided link. The study was submitted on Monday, 16th March 2026, at 08:47:28 and last modified on Tuesday, 17th March 2026, at 03:18:45. For additional resources, legal information, and portals, please visit CCSD’s website.
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