33 research outputs found
Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots
One of the open challenges in designing robots that operate successfully in
the unpredictable human environment is how to make them able to predict what
actions they can perform on objects, and what their effects will be, i.e., the
ability to perceive object affordances. Since modeling all the possible world
interactions is unfeasible, learning from experience is required, posing the
challenge of collecting a large amount of experiences (i.e., training data).
Typically, a manipulative robot operates on external objects by using its own
hands (or similar end-effectors), but in some cases the use of tools may be
desirable, nevertheless, it is reasonable to assume that while a robot can
collect many sensorimotor experiences using its own hands, this cannot happen
for all possible human-made tools.
Therefore, in this paper we investigate the developmental transition from
hand to tool affordances: what sensorimotor skills that a robot has acquired
with its bare hands can be employed for tool use? By employing a visual and
motor imagination mechanism to represent different hand postures compactly, we
propose a probabilistic model to learn hand affordances, and we show how this
model can generalize to estimate the affordances of previously unseen tools,
ultimately supporting planning, decision-making and tool selection tasks in
humanoid robots. We present experimental results with the iCub humanoid robot,
and we publicly release the collected sensorimotor data in the form of a hand
posture affordances dataset.Comment: dataset available at htts://vislab.isr.tecnico.ulisboa.pt/, IEEE
International Conference on Development and Learning and on Epigenetic
Robotics (ICDL-EpiRob 2017
A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots
This paper contributes towards the development and comparison of
Divergent-Component-of-Motion (DCM) based control architectures for humanoid
robot locomotion. More precisely, we present and compare several DCM based
implementations of a three layer control architecture. From top to bottom,
these three layers are here called: trajectory optimization, simplified model
control, and whole-body QP control. All layers use the DCM concept to generate
references for the layer below. For the simplified model control layer, we
present and compare both instantaneous and Receding Horizon Control
controllers. For the whole-body QP control layer, we present and compare
controllers for position and velocity control robots. Experimental results are
carried out on the one-meter tall iCub humanoid robot. We show which
implementation of the above control architecture allows the robot to achieve a
walking velocity of 0.41 meters per second.Comment: Submitted to Humanoids201
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central
importance to have good knowledge of both end-effector, object pose and shape.
In this work we propose a framework for markerless visual servoing on unknown
objects, which is divided in four main parts: I) a least-squares minimization
problem is formulated to find the volume of the object graspable by the robot's
hand using its stereo vision; II) a recursive Bayesian filtering technique,
based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
(position and orientation) of the robot's end-effector without the use of
markers; III) a nonlinear constrained optimization problem is formulated to
compute the desired graspable pose about the object; IV) an image-based visual
servo control commands the robot's end-effector toward the desired pose. We
demonstrate effectiveness and robustness of our approach with extensive
experiments on the iCub humanoid robot platform, achieving real-time
computation, smooth trajectories and sub-pixel precisions
Conducting neuropsychological tests with a humanoid robot: design and evaluation
International audience— Socially assistive robot with interactive behavioral capability have been improving quality of life for a wide range of users by taking care of elderlies, training individuals with cognitive disabilities or physical rehabilitation, etc. While the interactive behavioral policies of most systems are scripted, we discuss here key features of a new methodology that enables professional caregivers to teach a socially assistive robot (SAR) how to perform the assistive tasks while giving proper instructions, demonstrations and feedbacks. We describe here how socio-communicative gesture controllers – which actually control the speech, the facial displays and hand gestures of our iCub robot – are driven by multimodal events captured on a professional human demonstrator performing a neuropsychological interview. Furthermore, we propose an original online evaluation method for rating the multimodal interactive behaviors of the SAR and show how such a method can help designers to identify the faulty events
Inertial Parameter Identification Including Friction and Motor Dynamics
Identification of inertial parameters is fundamental for the implementation
of torque-based control in humanoids. At the same time, good models of friction
and actuator dynamics are critical for the low-level control of joint torques.
We propose a novel method to identify inertial, friction and motor parameters
in a single procedure. The identification exploits the measurements of the PWM
of the DC motors and a 6-axis force/torque sensor mounted inside the kinematic
chain. The partial least-square (PLS) method is used to perform the regression.
We identified the inertial, friction and motor parameters of the right arm of
the iCub humanoid robot. We verified that the identified model can accurately
predict the force/torque sensor measurements and the motor voltages. Moreover,
we compared the identified parameters against the CAD parameters, in the
prediction of the force/torque sensor measurements. Finally, we showed that the
estimated model can effectively detect external contacts, comparing it against
a tactile-based contact detection. The presented approach offers some
advantages with respect to other state-of-the-art methods, because of its
completeness (i.e. it identifies inertial, friction and motor parameters) and
simplicity (only one data collection, with no particular requirements).Comment: Pre-print of paper presented at Humanoid Robots, 13th IEEE-RAS
International Conference on, Atlanta, Georgia, 201
A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch
Multi-fingered robotic hands could enable robots to perform sophisticated
manipulation tasks. However, teaching a robot to grasp objects with an
anthropomorphic hand is an arduous problem due to the high dimensionality of
state and action spaces. Deep Reinforcement Learning (DRL) offers techniques to
design control policies for this kind of problems without explicit environment
or hand modeling. However, training these policies with state-of-the-art
model-free algorithms is greatly challenging for multi-fingered hands. The main
problem is that an efficient exploration of the environment is not possible for
such high-dimensional problems, thus causing issues in the initial phases of
policy optimization. One possibility to address this is to rely on off-line
task demonstrations. However, oftentimes this is incredibly demanding in terms
of time and computational resources. In this work, we overcome these
requirements and propose the A Grasp Pose is All You Need (G-PAYN) method for
the anthropomorphic hand of the iCub humanoid. We develop an approach to
automatically collect task demonstrations to initialize the training of the
policy. The proposed grasping pipeline starts from a grasp pose generated by an
external algorithm, used to initiate the movement. Then a control policy
(previously trained with the proposed G-PAYN) is used to reach and grab the
object. We deployed the iCub into the MuJoCo simulator and use it to test our
approach with objects from the YCB-Video dataset. The results show that G-PAYN
outperforms current DRL techniques in the considered setting, in terms of
success rate and execution time with respect to the baselines. The code to
reproduce the experiments will be released upon acceptance.Comment: Submitted to IROS 202