Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces

To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201

Investigating the brain mechanisms involved in learning abstract sensorimotor mappings

PhD ThesisMyoelectric-computer interfaces (MCIs) provide a unique opportunity to study mechanisms of motor learning and adaptation as they allow the creation of abstract sensorimotor tasks disassociated from biomechanical constraints, and the manipulation of visuomotor mappings at the level of individual muscles. In addition, study of MCI use provides a useful basis for designing optimal prosthetics, by understanding how the motor system deals with new patterns of muscle co-ordination. Here I used MCI tasks in order to examine subjects’ ability to learn and adapt to abstract sensorimotor mappings. In the tasks, subjects moved a 2D cursor controlled by electromyogram (EMG) recorded from between two and eight hand and forearm muscles. Each muscle was assigned a direction of action (DoA) and cursor position was determined using the vector sum of the EMG. Subjects were able to quickly learn abstract mappings, and adapt successfully to rotations of the full muscle-DoA mapping (global) and rotations where subsets of the muscle-DoA relationships were perturbed (local). Adaptation was biased by naturalistic behaviour, but that did not impede subjects from solving the tasks. Strategies that subjects used to solve local adaptation tasks could be biased via tDCS of M1 and the cerebellum. Global and local rotations were adapted to in different ways, with local adaptation lacking the after-effects associated with classical adaptation, indicating the creation of a new internal model for the adapted state, as opposed to alteration of a single one. tDCS affected these forms of adaptation in different ways, with stimulation of M1 predominantly affecting global adaptation and stimulation of the cerebellum predominantly affecting local adaptation. In conclusion, I have demonstrated that the motor system can successfully learn and adapt to abstract motor tasks, with the underlying processes being dependent on M1 and the cerebellum in ways that have a structural dependence

Efeitos de terapias orientadas pelo corpo nos sintomas negativos de pessoas com esquizofrenia: uma revisão sistemática

Background: numa fase estabilizada da esquizofrenia evidenciam-se os sintomas negativos, sobre os quais as terapias orientadas pelo corpo podem atuar. Objetivos: conhecer a força da evidência científica dos efeitos das terapias orientadas pelo corpo nos sintomas negativos de pessoas com esquizofrenia. Metodologia: a pesquisa realizou-se através do Pubmed, Cochrane, Web of Science, APAPsycNet, Science Direct, Scopus e Portal Regional da BVS. Foi avaliada a qualidade metodológica dos estudos através da escala do PEDro e realizada a síntese de dados. Resultados: 18 estudos incluídos investigaram as seguintes intervenções: artes criativas, mind-body e psicoterapia corporal. Foram investigados sintomas negativos (valor total), embotamento afetivo, anedonia, avolição, alogia, isolamento social e lentificação psicomotora. Conclusão: Há evidências científicas fortes que as terapias orientadas pelo corpo não promovem efeitos positivos na avolição, quando esta é avaliada através da escala SANS e que as artes criativas reduzem o valor total dos sintomas negativos, quando avaliado pela PANSS; Effects of body-oriented therapies on the negative symptoms of people with schizophrenia: a systematic review ABSTRACT: Background: in a stabilized phase of schizophrenia, negative symptoms are evident, on which body-oriented therapies can act. Objectives: to know the strength of scientific evidence of the effects of body-oriented therapies on the negative symptoms of people with schizophrenia. Methodology: the research was carried out through Pubmed, Cochrane, Web of Science, APAPsycNet, Science Direct, Scopus and the VHL Regional Portal. The methodological quality of the studies was assessed using the PEDro scale and data synthesis was performed. Results: 18 included studies investigated the following interventions: creative arts, mind-body and body psychotherapy. Negative symptoms (total value), affective blunting, anhedonia, avolition, alogia, asociality and psychomotor slowing were investigated. Conclusion: There is strong scientific evidence that body-oriented therapies do not promote positive effects on avolition when it is assessed using the SANS scale and that the creative arts reduce the total value of negative symptoms when assessed by PANSS

Generative Models for Learning Robot Manipulation Skills from Humans

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to direct trajectory learning from demonstrations, many problems arise in interactive robotic applications that require higher contextual level understanding of the environment. This requires learning invariant mappings in the demonstrations that can generalize across different environmental situations such as size, position, orientation of objects, viewpoint of the observer, etc. In this thesis, we address this challenge by encapsulating invariant patterns in the demonstrations using probabilistic learning models for acquiring dexterous manipulation skills. We learn the joint probability density function of the demonstrations with a hidden semi-Markov model, and smoothly follow the generated sequence of states with a linear quadratic tracking controller. The model exploits the invariant segments (also termed as sub-goals, options or actions) in the demonstrations and adapts the movement in accordance with the external environmental situations such as size, position and orientation of the objects in the environment using a task-parameterized formulation. We incorporate high-dimensional sensory data for skill acquisition by parsimoniously representing the demonstrations using statistical subspace clustering methods and exploit the coordination patterns in latent space. To adapt the models on the fly and/or teach new manipulation skills online with the streaming data, we formulate a non-parametric scalable online sequence clustering algorithm with Bayesian non-parametric mixture models to avoid the model selection problem while ensuring tractability under small variance asymptotics. We exploit the developed generative models to perform manipulation skills with remotely operated vehicles over satellite communication in the presence of communication delays and limited bandwidth. A set of task-parameterized generative models are learned from the demonstrations of different manipulation skills provided by the teleoperator. The model captures the intention of teleoperator on one hand and provides assistance in performing remote manipulation tasks on the other hand under varying environmental situations. The assistance is formulated under time-independent shared control, where the model continuously corrects the remote arm movement based on the current state of the teleoperator; and/or time-dependent autonomous control, where the model synthesizes the movement of the remote arm for autonomous skill execution. Using the proposed methodology with the two-armed Baxter robot as a mock-up for semi-autonomous teleoperation, we are able to learn manipulation skills such as opening a valve, pick-and-place an object by obstacle avoidance, hot-stabbing (a specialized underwater task akin to peg-in-a-hole task), screw-driver target snapping, and tracking a carabiner in as few as 4 - 8 demonstrations. Our study shows that the proposed manipulation assistance formulations improve the performance of the teleoperator by reducing the task errors and the execution time, while catering for the environmental differences in performing remote manipulation tasks with limited bandwidth and communication delays