Real-Time Dance Generation to Music for a Legged Robot
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Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...
Dynamic locomotion on unstructured and uneven terrain is a challenging task in legged robotics. Especially when it comes to slippery ground conditions, common state estimation and control algorithms suffer from the usual no-slip assumption. In fact, there ...
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Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanshi ...
The high agility of legged systems allows them to operate in rugged outdoor environments. In these situations, knowledge about the terrain geometry is key for foothold planning to enable safe locomotion. However, on penetrable or highly compliant terrain ( ...
Adapting to the ground enables stable footholds in legged locomotion by exploiting the structure of the terrain. On that account, we present a passive adaptive planar foot with three rotational degrees of freedom that is lightweight and thus suited for hig ...
Transferring solutions found by trajectory optimization to robotic hardware remains a challenging task. When the optimization fully exploits the provided model to perform dynamic tasks, the presence of unmodeled dynamics renders the motion infeasible on th ...
The computational power of mobile robots is currently insufficient to achieve torque level whole-body Model Predictive Control (MPC) at the update rates required for complex dynamic systems such as legged robots. This problem is commonly circumvented by us ...
