Author name cluster

Andreas ten Pas

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

IROS Conference 2021 Conference Paper

Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3)

Andreas ten Pas
Colin Keil
Robert Platt 0001

Grasp detection of novel objects in unstructured environments is a key capability in robotic manipulation. For 2D grasp detection problems where grasps are assumed to lie in the plane, it is common to design a fully convolutional neural network that predicts grasps over an entire image in one step. However, this is not possible for grasp pose detection where grasp poses are assumed to exist in SE(3). In this case, it is common to approach the problem in two steps: grasp candidate generation and candidate classification [1], [2], [3], [4]. Since grasp candidate classification is typically expensive, the problem becomes one of efficiently identifying high quality candidate grasps. This paper proposes a new grasp candidate generation method that significantly outperforms major 3D grasp detection baselines. Supplementary material is available at this website.

Details

ICRA Conference 2019 Conference Paper

Online Planning for Target Object Search in Clutter under Partial Observability

Yuchen Xiao
Sammie Katt
Andreas ten Pas
Shengjian Chen
Christopher Amato

The problem of finding and grasping a target object in a cluttered, uncertain environment, target object search, is a common and important problem in robotics. One key challenge is the uncertainty of locating and recognizing each object in a cluttered environment due to noisy perception and occlusions. Furthermore, the uncertainty in localization makes manipulation difficult and uncertain. To cope with these challenges, we formulate the target object search task as a partially observable Markov decision process (POMDP), enabling the robot to reason about perceptual and manipulation uncertainty while searching. To further address the manipulation difficulty, we propose Parameterized Action Partially Observable Monte-Carlo Planning (PA-POMCP), an algorithm that evaluates manipulation actions by taking into account the effect of the robot's current belief on the success of the action execution. In addition, a novel run-time initial belief generator and a state value estimator are introduced in this paper to facilitate the PA-POMCP algorithm. Our experiments show that our methods solve the target object search task in settings where simpler methods either take more object movements or fail.

Details

ICRA Conference 2018 Conference Paper

Pick and Place Without Geometric Object Models

Marcus Gualtieri
Andreas ten Pas
Robert Platt 0001

We propose a novel formulation of robotic pick and place as a deep reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation. In this formulation, actions are target reach poses for the hand and states are a history of such reaches. We show this approach can solve a challenging class of pick-place and regrasping problems where the exact geometry of the objects to be handled is unknown. The only information our method requires is: 1) the sensor perception available to the robot at test time; 2) prior knowledge of the general class of objects for which the system was trained. We evaluate our method using objects belonging to two different categories, mugs and bottles, both in simulation and on real hardware. Results show a major improvement relative to a shape primitives baseline.

Details

ICRA Conference 2017 Conference Paper

Open world assistive grasping using laser selection

Marcus Gualtieri
James Kuczynski
Abraham M. Shultz
Andreas ten Pas
Robert Platt 0001
Holly A. Yanco

Many people with motor disabilities are unable to complete activities of daily living (ADLs) without assistance. This paper describes a complete robotic system developed to provide mobile grasping assistance for ADLs. The system is comprised of a robot arm from a Rethink Robotics Baxter robot mounted to an assistive mobility device, a control system for that arm, and a user interface with a variety of access methods for selecting desired objects. The system uses grasp detection to allow previously unseen objects to be picked up by the system. The grasp detection algorithms also allow for objects to be grasped in cluttered environments. We evaluate our system in a number of experiments on a large variety of objects. Overall, we achieve an object selection success rate of 88% and a grasp detection success rate of 90% in a non-mobile scenario, and success rates of 89% and 72% in a mobile scenario.

Details

IROS Conference 2016 Conference Paper

High precision grasp pose detection in dense clutter

Marcus Gualtieri
Andreas ten Pas
Kate Saenko
Robert Platt 0001

This paper considers the problem of grasp pose detection in point clouds. We follow a general algorithmic structure that first generates a large set of 6-DOF grasp candidates and then classifies each of them as a good or a bad grasp. Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network. We propose two new representations of grasp candidates, and we quantify the effect of using prior knowledge of two forms: instance or category knowledge of the object to be grasped, and pretraining the network on simulated depth data obtained from idealized CAD models. Our analysis shows that a more informative grasp candidate representation as well as pretraining and prior knowledge significantly improve grasp detection. We evaluate our approach on a Baxter Research Robot and demonstrate an average grasp success rate of 93% in dense clutter. This is a 20% improvement compared to our prior work.

Details

IROS Conference 2015 Conference Paper

The Baxter Easyhand: A robot hand that costs $150 US in parts

Giulia Franchi
Andreas ten Pas
Robert Platt 0001
Stefano Panzieri

This paper introduces and characterizes the Baxter Easyhand, a new 3D printed hand derived from the Yale T42 hand [5], [10], but designed specifically to be mounted on the Baxter robot from Rethink robotics. Because this hand is designed specifically for Baxter, we are able to make some important simplifications in the design relative to other 3D printed hands. In particular, the Easyhand is smaller than most other 3D printed hands and it is powered by the native Baxter gripper actuator. As a result, our hand is cheaper, lighter, and easier to interface with than other robot hands available for Baxter. This paper details the design of the hand and its mechanical characteristics and reports results from experiments that characterize its grasping performance.

Details

IROS Conference 2014 Conference Paper

Localization and manipulation of small parts using GelSight tactile sensing

Rui Li 0017
Robert Platt 0001
Wenzhen Yuan 0001
Andreas ten Pas
Nathan Roscup
Mandayam A. Srinivasan
Edward H. Adelson

Robust manipulation and insertion of small parts can be challenging because of the small tolerances typically involved. The key to robust control of these kinds of manipulation interactions is accurate tracking and control of the parts involved. Typically, this is accomplished using visual servoing or force-based control. However, these approaches have drawbacks. Instead, we propose a new approach that uses tactile sensing to accurately localize the pose of a part grasped in the robot hand. Using a feature-based matching technique in conjunction with a newly developed tactile sensing technology known as GelSight that has much higher resolution than competing methods, we synthesize high-resolution height maps of object surfaces. As a result of these high-resolution tactile maps, we are able to localize small parts held in a robot hand very accurately. We quantify localization accuracy in benchtop experiments and experimentally demonstrate the practicality of the approach in the context of a small parts insertion problem.

Details