48,619 research outputs found
Constrained DMPs for Feasible Skill Learning on Humanoid Robots
In the context of humanoid skill learning, movement primitives have gained much attention because of their compact representation and convenient combination with a myriad of optimization approaches. Among them, a well-known scheme is to use Dynamic Movement Primitives (DMPs) with reinforcement learning (RL) algorithms. While various remarkable results have been reported, skill learning with physical constraints has not been sufficiently investigated. For example, when RL is employed to optimize the robot joint trajectories, the exploration noise could drive the resulting trajectory out of the joint limits. In this paper, we focus on robot skill learning characterized by joint limit avoidance, by introducing the novel Constrained Dynamic Movement Primitives (CDMPs). By controlling a set of transformed states (called exogenous states) instead of the original DMPs states, CDMPs are capable of maintaining the joint trajectories within the safety limits. We validate CDMPs on the humanoid robot iCub, showing the applicability of our approach
A Multi-disciplinary Approach to the Investigation of Aspects of Serial Order in Cognition
Serial order processing or Sequence processing underlies many human activities such as speech, language, skill learning, planning, problem solving, etc. Investigating the\ud
neural bases of sequence processing enables us to understand serial order in cognition and helps us building intelligent devices. In the current paper, various\ud
cognitive issues related to sequence processing will be discussed with examples. Some of the issues are: distributed versus local representation, pre-wired versus\ud
adaptive origins of representation, implicit versus explicit learning, fixed/flat versus hierarchical organization, timing aspects, order information embedded in sequences, primacy versus recency in list learning and aspects of sequence perception such as recognition, recall and generation. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, theoretical frameworks based on Markov models and Reinforcement Learning paradigm will be presented. These theoretical ideas are useful for studying sequential phenomena in a principled way
Robot Composite Learning and the Nunchaku Flipping Challenge
Advanced motor skills are essential for robots to physically coexist with
humans. Much research on robot dynamics and control has achieved success on
hyper robot motor capabilities, but mostly through heavily case-specific
engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous
manner, robot learning from human demonstration (LfD) has achieved great
progress, but still has limitations handling dynamic skills and compound
actions. In this paper, we present a composite learning scheme which goes
beyond LfD and integrates robot learning from human definition, demonstration,
and evaluation. The method tackles advanced motor skills that require dynamic
time-critical maneuver, complex contact control, and handling partly soft
partly rigid objects. We also introduce the "nunchaku flipping challenge", an
extreme test that puts hard requirements to all these three aspects. Continued
from our previous presentations, this paper introduces the latest update of the
composite learning scheme and the physical success of the nunchaku flipping
challenge
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