Topic: dexterous object manipulation

Building realistic models for complex and skilful motor tasks such as dexterous object manipulation is hampered by two key challenges: the need for physiologically-detailed musculoskeletal models and powerful algorithms that are able to tackle such high-dimensional problems. The newly released MyoSuite ecosystem provides such musculoskeletal models and is 4000x faster than previous simulators, thereby enabling us to develop and test algorithms for agile object manipulation. Combining ideas from stochastic optimal control and reinforcement learning (RL), we develop a training curriculum called

Efficient, physiologically-detailed musculoskeletal simulators and powerful learning algorithms provide new computational tools to tackle the grand challenge of understanding biological motor control. Our winning solution for the first NeurIPS MyoChallenge leverages an approach mirroring human learning and showcases reinforcement and curriculum learning as mechanisms to find motor control policies in complex object manipulation tasks. The training procedure, which we call static-to-dynamic stability (SDS), defines a curriculum of progressively more complex state-space neighborhoods, which guid

Dexterous object manipulation with the freedom of choice of contact points has primarily been shown to elicit a feedback-dependent force control as opposed to the classically studied memory representation-based force control for constrained grasping. We probed this dichotomy in sensorimotor integration by introducing uncertainty about the grasp type during movement preparation within the context of motor planning. An inverted T-shaped instrumented manipulandum was presented to 24 participants, and they were asked to reach, grasp, and lift it while minimising the peak roll. On certain catch tri