42 research outputs found
Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning
A mechanism called Eligibility Propagation is proposed to speed up the Time
Hopping technique used for faster Reinforcement Learning in simulations.
Eligibility Propagation provides for Time Hopping similar abilities to what
eligibility traces provide for conventional Reinforcement Learning. It
propagates values from one state to all of its temporal predecessors using a
state transitions graph. Experiments on a simulated biped crawling robot
confirm that Eligibility Propagation accelerates the learning process more than
3 times.Comment: 7 page
Exploring Restart Distributions
We consider the generic approach of using an experience memory to help
exploration by adapting a restart distribution. That is, given the capacity to
reset the state with those corresponding to the agent's past observations, we
help exploration by promoting faster state-space coverage via restarting the
agent from a more diverse set of initial states, as well as allowing it to
restart in states associated with significant past experiences. This approach
is compatible with both on-policy and off-policy methods. However, a caveat is
that altering the distribution of initial states could change the optimal
policies when searching within a restricted class of policies. To reduce this
unsought learning bias, we evaluate our approach in deep reinforcement learning
which benefits from the high representational capacity of deep neural networks.
We instantiate three variants of our approach, each inspired by an idea in the
context of experience replay. Using these variants, we show that performance
gains can be achieved, especially in hard exploration problems.Comment: RLDM 201
The Hydra Hand: A Mode-Switching Underactuated Gripper with Precision and Power Grasping Modes
Human hands are able to grasp a wide range of object sizes, shapes, and
weights, achieved via reshaping and altering their apparent grasping stiffness
between compliant power and rigid precision. Achieving similar versatility in
robotic hands remains a challenge, which has often been addressed by adding
extra controllable degrees of freedom, tactile sensors, or specialised extra
grasping hardware, at the cost of control complexity and robustness. We
introduce a novel reconfigurable four-fingered two-actuator underactuated
gripper -- the Hydra Hand -- that switches between compliant power and rigid
precision grasps using a single motor, while generating grasps via a single
hydraulic actuator -- exhibiting adaptive grasping between finger pairs,
enabling the power grasping of two objects simultaneously. The mode switching
mechanism and the hand's kinematics are presented and analysed, and performance
is tested on two grasping benchmarks: one focused on rigid objects, and the
other on items of clothing. The Hydra Hand is shown to excel at grasping large
and irregular objects, and small objects with its respective compliant power
and rigid precision configurations. The hand's versatility is then showcased by
executing the challenging manipulation task of safely grasping and placing a
bunch of grapes, and then plucking a single grape from the bunch.Comment: This paper has been accepted for publication in IEEE Robotics and
Automation Letters. For the purpose of open access, the author(s) has applied
a Creative Commons Attribution (CC BY) license to any Accepted Manuscript
version arising. 8 pages, 11 figure