438 research outputs found
Learning Human-Robot Collaboration Insights through the Integration of Muscle Activity in Interaction Motion Models
Recent progress in human-robot collaboration makes fast and fluid
interactions possible, even when human observations are partial and occluded.
Methods like Interaction Probabilistic Movement Primitives (ProMP) model human
trajectories through motion capture systems. However, such representation does
not properly model tasks where similar motions handle different objects. Under
current approaches, a robot would not adapt its pose and dynamics for proper
handling. We integrate the use of Electromyography (EMG) into the Interaction
ProMP framework and utilize muscular signals to augment the human observation
representation. The contribution of our paper is increased task discernment
when trajectories are similar but tools are different and require the robot to
adjust its pose for proper handling. Interaction ProMPs are used with an
augmented vector that integrates muscle activity. Augmented time-normalized
trajectories are used in training to learn correlation parameters and robot
motions are predicted by finding the best weight combination and temporal
scaling for a task. Collaborative single task scenarios with similar motions
but different objects were used and compared. For one experiment only joint
angles were recorded, for the other EMG signals were additionally integrated.
Task recognition was computed for both tasks. Observation state vectors with
augmented EMG signals were able to completely identify differences across
tasks, while the baseline method failed every time. Integrating EMG signals
into collaborative tasks significantly increases the ability of the system to
recognize nuances in the tasks that are otherwise imperceptible, up to 74.6% in
our studies. Furthermore, the integration of EMG signals for collaboration also
opens the door to a wide class of human-robot physical interactions based on
haptic communication that has been largely unexploited in the field.Comment: 7 pages, 2 figures, 2 tables. As submitted to Humanoids 201
Policy Learning with Hypothesis based Local Action Selection
For robots to be able to manipulate in unknown and unstructured environments
the robot should be capable of operating under partial observability of the
environment. Object occlusions and unmodeled environments are some of the
factors that result in partial observability. A common scenario where this is
encountered is manipulation in clutter. In the case that the robot needs to
locate an object of interest and manipulate it, it needs to perform a series of
decluttering actions to accurately detect the object of interest. To perform
such a series of actions, the robot also needs to account for the dynamics of
objects in the environment and how they react to contact. This is a non trivial
problem since one needs to reason not only about robot-object interactions but
also object-object interactions in the presence of contact. In the example
scenario of manipulation in clutter, the state vector would have to account for
the pose of the object of interest and the structure of the surrounding
environment. The process model would have to account for all the aforementioned
robot-object, object-object interactions. The complexity of the process model
grows exponentially as the number of objects in the scene increases. This is
commonly the case in unstructured environments. Hence it is not reasonable to
attempt to model all object-object and robot-object interactions explicitly.
Under this setting we propose a hypothesis based action selection algorithm
where we construct a hypothesis set of the possible poses of an object of
interest given the current evidence in the scene and select actions based on
our current set of hypothesis. This hypothesis set tends to represent the
belief about the structure of the environment and the number of poses the
object of interest can take. The agent's only stopping criterion is when the
uncertainty regarding the pose of the object is fully resolved.Comment: RLDM abstrac
Learning modular policies for robotics
A promising idea for scaling robot learning to more complex tasks is to use elemental behaviors as building blocks to compose more complex behavior. Ideally, such building blocks are used in combination with a learning algorithm that is able to learn to select, adapt, sequence and co-activate the building blocks. While there has been a lot of work on approaches that support one of these requirements, no learning algorithm exists that unifies all these properties in one framework. In this paper we present our work on a unified approach for learning such a modular control architecture. We introduce new policy search algorithms that are based on information-theoretic principles and are able to learn to select, adapt and sequence the building blocks. Furthermore, we developed a new representation for the individual building block that supports co-activation and principled ways for adapting the movement. Finally, we summarize our experiments for learning modular control architectures in simulation and with real robots
Non-parametric Imitation Learning of Robot Motor Skills
Unstructured environments impose several challenges when robots are required to perform different tasks and adapt to unseen situations. In this context, a relevant problem arises: how can robots learn to perform various tasks and adapt to different conditions? A potential solution is to endow robots with learning capabilities. In this line, imitation learning emerges as an intuitive way to teach robots different motor skills. This learning approach typically mimics human demonstrations by extracting invariant motion patterns and subsequently applies these patterns to new situations. In this paper, we propose a novel kernel treatment of imitation learning, which endows the robot with imitative and adaptive capabilities. In particular, due to the kernel treatment, the proposed approach is capable of learning human skills associated with high-dimensional inputs. Furthermore, we study a new concept of correlation-adaptive imitation learning, which allows for the adaptation of correlations exhibited in high-dimensional demonstrated skills. Several toy examples and a collaborative task with a real robot are provided to verify the effectiveness of our approach
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