14 research outputs found
Laboratory Automation: Precision Insertion with Adaptive Fingers utilizing Contact through Sliding with Tactile-based Pose Estimation
Micro well-plates are commonly used apparatus in chemical and biological
experiments that are a few centimeters in thickness with wells in them. The
task we aim to solve is to place (insert) them onto a well-plate holder with
grooves a few millimeters in height. Our insertion task has the following
facets: 1) There is uncertainty in the detection of the position and pose of
the well-plate and well-plate holder, 2) the accuracy required is in the order
of millimeter to sub-millimeter, 3) the well-plate holder is not fastened, and
moves with external force, 4) the groove is shallow, and 5) the width of the
groove is small. Addressing these challenges, we developed a) an adaptive
finger gripper with accurate detection of finger position (for (1)), b) grasped
object pose estimation using tactile sensors (for (1)), c) a method to insert
the well-plate into the target holder by sliding the well-plate while
maintaining contact with the edge of the holder (for (2-4)), and d) estimating
the orientation of the edge and aligning the well-plate so that the holder does
not move when maintaining contact with the edge (for (5)). We show a
significantly high success rate on the insertion task of the well-plate, even
though under added noise.
An accompanying video is available at the following link:
https://drive.google.com/file/d/1UxyJ3XIxqXPnHcpfw-PYs5T5oYQxoc6i/view?usp=sharingComment: 7 pages, 5 figure
Towards Generalized Robot Assembly through Compliance-Enabled Contact Formations
Contact can be conceptualized as a set of constraints imposed on two bodies
that are interacting with one another in some way. The nature of a contact,
whether a point, line, or surface, dictates how these bodies are able to move
with respect to one another given a force, and a set of contacts can provide
either partial or full constraint on a body's motion. Decades of work have
explored how to explicitly estimate the location of a contact and its dynamics,
e.g., frictional properties, but investigated methods have been computationally
expensive and there often exists significant uncertainty in the final
calculation. This has affected further advancements in contact-rich tasks that
are seemingly simple to humans, such as generalized peg-in-hole insertions. In
this work, instead of explicitly estimating the individual contact dynamics
between an object and its hole, we approach this problem by investigating
compliance-enabled contact formations. More formally, contact formations are
defined according to the constraints imposed on an object's available
degrees-of-freedom. Rather than estimating individual contact positions, we
abstract out this calculation to an implicit representation, allowing the robot
to either acquire, maintain, or release constraints on the object during the
insertion process, by monitoring forces enacted on the end effector through
time. Using a compliant robot, our method is desirable in that we are able to
complete industry-relevant insertion tasks of tolerances <0.25mm without prior
knowledge of the exact hole location or its orientation. We showcase our method
on more generalized insertion tasks, such as commercially available
non-cylindrical objects and open world plug tasks
PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation
The study addresses the foundational and challenging task of peg-in-hole
assembly in robotics, where misalignments caused by sensor inaccuracies and
mechanical errors often result in insertion failures or jamming. This research
introduces PolyFit, representing a paradigm shift by transitioning from a
reinforcement learning approach to a supervised learning methodology. PolyFit
is a Force/Torque (F/T)-based supervised learning framework designed for 5-DoF
peg-in-hole assembly. It utilizes F/T data for accurate extrinsic pose
estimation and adjusts the peg pose to rectify misalignments. Extensive
training in a simulated environment involves a dataset encompassing a diverse
range of peg-hole shapes, extrinsic poses, and their corresponding contact F/T
readings. To enhance extrinsic pose estimation, a multi-point contact strategy
is integrated into the model input, recognizing that identical F/T readings can
indicate different poses. The study proposes a sim-to-real adaptation method
for real-world application, using a sim-real paired dataset to enable effective
generalization to complex and unseen polygon shapes. PolyFit achieves
impressive peg-in-hole success rates of 97.3% and 96.3% for seen and unseen
shapes in simulations, respectively. Real-world evaluations further demonstrate
substantial success rates of 86.7% and 85.0%, highlighting the robustness and
adaptability of the proposed method.Comment: 8 pages, 8 figures, 3 table
Bridging the Sim-to-Real Gap with Dynamic Compliance Tuning for Industrial Insertion
Contact-rich manipulation tasks often exhibit a large sim-to-real gap. For
instance, industrial assembly tasks frequently involve tight insertions where
the clearance is less than mm and can even be negative when dealing
with a deformable receptacle. This narrow clearance leads to complex contact
dynamics that are difficult to model accurately in simulation, making it
challenging to transfer simulation-learned policies to real-world robots. In
this paper, we propose a novel framework for robustly learning manipulation
skills for real-world tasks using only the simulated data. Our framework
consists of two main components: the ``Force Planner'' and the ``Gain Tuner''.
The Force Planner is responsible for planning both the robot motion and desired
contact forces, while the Gain Tuner dynamically adjusts the compliance control
gains to accurately track the desired contact forces during task execution. The
key insight of this work is that by adaptively adjusting the robot's compliance
control gains during task execution, we can modulate contact forces in the new
environment, thereby generating trajectories similar to those trained in
simulation and narrows the sim-to-real gap. Experimental results show that our
method, trained in simulation on a generic square peg-and-hole task, can
generalize to a variety of real-world insertion tasks involving narrow or even
negative clearances, all without requiring any fine-tuning
Safely Learning Visuo-Tactile Feedback Policies in Real For Industrial Insertion
Industrial insertion tasks are often performed repetitively with parts that
are subject to tight tolerances and prone to breakage. In this paper, we
present a safe method to learn a visuo-tactile insertion policy that is robust
against grasp pose variations while minimizing human inputs and collision
between the robot and the environment. We achieve this by dividing the
insertion task into two phases. In the first align phase, we learn a
tactile-based grasp pose estimation model to align the insertion part with the
receptacle. In the second insert phase, we learn a vision-based policy to guide
the part into the receptacle. Using force-torque sensing, we also develop a
safe self-supervised data collection pipeline that limits collision between the
part and the surrounding environment. Physical experiments on the USB insertion
task from the NIST Assembly Taskboard suggest that our approach can achieve
45/45 insertion successes on 45 different initial grasp poses, improving on two
baselines: (1) a behavior cloning agent trained on 50 human insertion
demonstrations (1/45) and (2) an online RL policy (TD3) trained in real (0/45)
Robotic Assembly Control Reconfiguration Based on Transfer Reinforcement Learning for Objects with Different Geometric Features
Robotic force-based compliance control is a preferred approach to achieve
high-precision assembly tasks. When the geometric features of assembly objects
are asymmetric or irregular, reinforcement learning (RL) agents are gradually
incorporated into the compliance controller to adapt to complex force-pose
mapping which is hard to model analytically. Since force-pose mapping is
strongly dependent on geometric features, a compliance controller is only
optimal for current geometric features. To reduce the learning cost of assembly
objects with different geometric features, this paper is devoted to answering
how to reconfigure existing controllers for new assembly objects with different
geometric features. In this paper, model-based parameters are first
reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL).
Then the RL agent is transferred based on the proposed Weighted Dimensional
Policy Distillation (WDPD) method. The experiment results demonstrate that the
control reconfiguration method costs less time and achieves better control
performance, which confirms the validity of proposed methods