237 research outputs found
Reconstructing null-space policies subject to dynamic task constraints in redundant manipulators
We consider the problem of direct policy learning in situations where the policies are only observable through their projections into the null-space of a set of dynamic, non-linear task constraints. We tackle the issue of deriving consistent data for the learning of such policies and make two contributions towards its solution. Firstly, we derive the conditions required to exactly reconstruct null-space policies and suggest a learning strategy based on this derivation. Secondly, we consider the case that the null-space policy is conservative and show that such a policy can be learnt more easily and robustly by learning the underlying potential function and using this as our representation of the policy.
Stability of Surface Contacts for Humanoid Robots: Closed-Form Formulae of the Contact Wrench Cone for Rectangular Support Areas
Humanoid robots locomote by making and breaking contacts with their
environment. A crucial problem is therefore to find precise criteria for a
given contact to remain stable or to break. For rigid surface contacts, the
most general criterion is the Contact Wrench Condition (CWC). To check whether
a motion satisfies the CWC, existing approaches take into account a large
number of individual contact forces (for instance, one at each vertex of the
support polygon), which is computationally costly and prevents the use of
efficient inverse-dynamics methods. Here we argue that the CWC can be
explicitly computed without reference to individual contact forces, and give
closed-form formulae in the case of rectangular surfaces -- which is of
practical importance. It turns out that these formulae simply and naturally
express three conditions: (i) Coulomb friction on the resultant force, (ii) ZMP
inside the support area, and (iii) bounds on the yaw torque. Conditions (i) and
(ii) are already known, but condition (iii) is, to the best of our knowledge,
novel. It is also of particular interest for biped locomotion, where undesired
foot yaw rotations are a known issue. We also show that our formulae yield
simpler and faster computations than existing approaches for humanoid motions
in single support, and demonstrate their consistency in the OpenHRP simulator.Comment: 14 pages, 4 figure
Efficient Geometric Linearization of Moving-Base Rigid Robot Dynamics
The linearization of the equations of motion of a robotics system about a
given state-input trajectory, including a controlled equilibrium state, is a
valuable tool for model-based planning, closed-loop control, gain tuning, and
state estimation. Contrary to the case of fixed based manipulators with
prismatic or rotary joints, the state space of moving-base robotic systems such
as humanoids, quadruped robots, or aerial manipulators cannot be globally
parametrized by a finite number of independent coordinates. This impossibility
is a direct consequence of the fact that the state of these systems includes
the system's global orientation, formally described as an element of the
special orthogonal group SO(3). As a consequence, obtaining the linearization
of the equations of motion for these systems is typically resolved, from a
practical perspective, by locally parameterizing the system's attitude by means
of, e.g., Euler or Cardan angles. This has the drawback, however, of
introducing artificial parameterization singularities and extra derivative
computations. In this contribution, we show that it is actually possible to
define a notion of linearization that does not require the use of a local
parameterization for the system's orientation, obtaining a mathematically
elegant, recursive, and singularity-free linearization for moving-based robot
systems. Recursiveness, in particular, is obtained by proposing a nontrivial
modification of existing recursive algorithms to allow for computations of the
geometric derivatives of the inverse dynamics and the inverse of the mass
matrix of the robotic system. The correctness of the proposed algorithm is
validated by means of a numerical comparison with the result obtained via
geometric finite difference
Planning and Control Strategies for Motion and Interaction of the Humanoid Robot COMAN+
Despite the majority of robotic platforms are still confined in controlled environments such as factories, thanks to the ever-increasing level of autonomy and the progress on human-robot interaction, robots are starting to be employed for different operations, expanding their focus from uniquely industrial to more diversified scenarios.
Humanoid research seeks to obtain the versatility and dexterity of robots capable of mimicking human motion in any environment. With the aim of operating side-to-side with humans, they should be able to carry out complex tasks without posing a threat during operations.
In this regard, locomotion, physical interaction with the environment and safety are three essential skills to develop for a biped.
Concerning the higher behavioural level of a humanoid, this thesis addresses both ad-hoc movements generated for specific physical interaction tasks and cyclic movements for locomotion. While belonging to the same category and sharing some of the theoretical obstacles, these actions require different approaches: a general high-level task is composed of specific movements that depend on the environment and the nature of the task itself, while regular locomotion involves the generation of periodic trajectories of the limbs.
Separate planning and control architectures targeting these aspects of biped motion are designed and developed both from a theoretical and a practical standpoint, demonstrating their efficacy on the new humanoid robot COMAN+, built at Istituto Italiano di Tecnologia.
The problem of interaction has been tackled by mimicking the intrinsic elasticity of human muscles, integrating active compliant controllers. However, while state-of-the-art robots may be endowed with compliant architectures, not many can withstand potential system failures that could compromise the safety of a human interacting with the robot. This thesis proposes an implementation of such low-level controller that guarantees a fail-safe behaviour, removing the threat that a humanoid robot could pose if a system failure occurred
Reconstructing Null-space Policies Subject to Dynamic Task Constraints in Redundant Manipulators
We consider the problem of direct policy learning in situations where the policies are only
observable through their projections into the null-space of a set of dynamic, non-linear
task constraints. We tackle the issue of deriving consistent data for the learning of such
policies and make two contributions towards its solution. Firstly, we derive the conditions
required to exactly reconstruct null-space policies and suggest a learning strategy based on
this derivation. Secondly, we consider the case that the null-space policy is conservative
and show that such a policy can be learnt more easily and robustly by learning the
underlying potential function and using this as our representation of the policy
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