159 research outputs found
Definition and composition of motor primitives using latent force models and hidden Markov models
In this work a different probabilistic motor primitive parameterization is proposed using latent force models (LFMs). The sequential composition of different motor primitives is also addressed using hidden Markov models (HMMs) which allows to capture the redundancy over dynamics by using a limited set of hidden primitives. The capability of the proposed model to learn and identify motor primitive occurrences over unseen movement realizations is validated using synthetic and motion capture data
Learning and Composing Primitive Skills for Dual-Arm Manipulation
In an attempt to confer robots with complex manipulation capabilities,
dual-arm anthropomorphic systems have become an important research topic in the
robotics community. Most approaches in the literature rely upon a great
understanding of the dynamics underlying the system's behaviour and yet offer
limited autonomous generalisation capabilities. To address these limitations,
this work proposes a modelisation for dual-arm manipulators based on dynamic
movement primitives laying in two orthogonal spaces. The modularity and
learning capabilities of this model are leveraged to formulate a novel
end-to-end learning-based framework which (i) learns a library of primitive
skills from human demonstrations, and (ii) composes such knowledge
simultaneously and sequentially to confront novel scenarios. The feasibility of
the proposal is evaluated by teaching the iCub humanoid the basic skills to
succeed on simulated dual-arm pick-and-place tasks. The results suggest the
learning and generalisation capabilities of the proposed framework extend to
autonomously conduct undemonstrated dual-arm manipulation tasks.Comment: Annual Conference Towards Autonomous Robotic Systems (TAROS19
Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds
The complexity of a legged robot's environment or task can inform how
specialised its gait must be to ensure success. Evolving specialised robotic
gaits demands many evaluations - acceptable for computer simulations, but not
for physical robots. For some tasks, a more general gait, with lower
optimization costs, could be satisfactory. In this paper, we introduce a new
type of gait controller where complexity can be set by a single parameter,
using a dynamic genotype-phenotype mapping. Low controller complexity leads to
conservative gaits, while higher complexity allows more sophistication and high
performance for demanding tasks, at the cost of optimization effort. We
investigate the new controller on a virtual robot in simulations and do
preliminary testing on a real-world robot. We show that having variable
complexity allows us to adapt to different optimization budgets. With a high
evaluation budget in simulation, a complex controller performs best. Moreover,
real-world evolution with a limited evaluation budget indicates that a lower
gait complexity is preferable for a relatively simple environment.Comment: Accepted to EvoApplications1
Can small scale search behaviours enhance large-scale navigation?
We develop a spiking neural network model of an insect-inspired CPG which is used to underpin steering behaviour for a Braitenberg-like vehicle. We show that small scale search behaviour, produced by the CPG, improves navigation by recovering useful sensory signals
Probabilistic movement primitives for coordination of multiple humanârobot collaborative tasks
This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and humanârobot movement coordination. It uses imitation learning to construct a mixture model of humanârobot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the humanârobot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories
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