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Clemens Eppner

24 accepted papers

2026

Grasp-MPC: Closed-Loop Visual Grasping Via Value-Guided Model Predictive Control

Jun Yamada, Adithyavairavan Murali, Ajay Uday Mandlekar, Clemens Eppner, Ingmar Posner, Balakumar Sundaralingam
ICRA 2026poster

Grasping of diverse objects in unstructured environments remains a significant challenge. Open-loop grasping methods, effective in controlled settings, struggle in cluttered environments. Grasp prediction errors and object pose changes during grasping are the main causes of failure. In contrast, clo…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2026

GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping

Beining Han, Yu-Wei Chao, Erwin Coumans, Clemens Eppner, Jia Deng, Stan Birchfield +1
CVPR 2026

We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and physical grasping processes. Our method extends diffusion model based generative 6-DOF grasping models to condition on

Cited by 0Source ↗code ↗Scholar ↗
2026

GraspGen: A Diffusion-Based Framework for 6-DOF Grasping with On-Generator Training

Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao, Wentao Yuan, Jun Yamada, Mark Carlson +4
ICRA 2026poster

Grasping is a fundamental robot skill, yet despite significant research advancements, learning-based 6-DOF grasping approaches are still not turnkey and struggle to generalize across different embodiments and in-the-wild settings. We build upon the recent success on modeling the object-centric grasp…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability

Xiaolin Fang, Caelan Reed Garrett, Clemens Eppner, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Dieter Fox
IROS 2024poster

Generative models such as diffusion models, excel at capturing high-dimensional distributions with diverse input modalities, e.g. robot trajectories, but are less effective at multistep constraint reasoning. Task and Motion Planning (TAMP) approaches are suited for planning multi-step autonomous rob…

Cited by 21Source ↗Scholar ↗
2024

One-Shot Transfer of Long-Horizon Extrinsic Manipulation Through Contact Retargeting

Albert Wu, Ruocheng Wang, Sirui Chen, Clemens Eppner, C. Karen Liu
IROS 2024

Extrinsic manipulation, the use of environment contacts to achieve manipulation objectives, enables strategies that are otherwise impossible with a parallel jaw gripper. However, orchestrating a long-horizon sequence of contact interactions between the robot, object, and environment is notoriously c

Cited by 11Source ↗Scholar ↗
2023

CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation

Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Adam Fishman, Dieter Fox
ICRA 2023poster

We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes-orders of magnitude more than prior work-in diverse everyday environments, such as cabinets and shelves. We render synthetic partial poin…

Cited by 27Source ↗code ↗Scholar ↗
2022

DefGraspSim: Physics-Based Simulation of Grasp Outcomes for 3D Deformable Objects

Isabella Huang, Yashraj Narang, Clemens Eppner, Balakumar Sundaralingam, Miles Macklin, Ruzena Bajcsy +2
RA-L 2022

Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery, and household automation. However, developing grasp strategies for such objects is uniquely challenging. Unlike rigid

Cited by 37Source ↗Scholar ↗
2022

Motion Policy Networks

Adam Fishman, Adithyavairavan Murali, Clemens Eppner, Bryan Peele, Byron Boots, Dieter Fox
CoRL 2022poster

Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not only should the solutions be optimal, the motion generator itself must be fast enough for real-time performance and reliab…

Cited by 66Source ↗code ↗Scholar ↗
2021

Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments

Fahad Islam, Chris Paxton, Clemens Eppner, Bryan Peele, Maxim Likhachev, Dieter Fox
ICRA 2021poster

In many applications, including logistics and manufacturing, robot manipulators operate in semi-structured environments alongside humans or other robots. These environments are largely static, but they may contain some movable obstacles that the robot must avoid. Manipulation tasks in these applicat…

Cited by 8Source ↗Scholar ↗
2021

Object Rearrangement Using Learned Implicit Collision Functions

Michael Danielczuk, Arsalan Mousavian, Clemens Eppner, Dieter Fox
ICRA 2021poster

Robotic object rearrangement combines the skills of picking and placing objects. When object models are unavailable, typical collision-checking models may be unable to predict collisions in partial point clouds with occlusions, making generation of collision-free grasping or placement trajectories c…

Cited by 97Source ↗Scholar ↗
2020

6-DOF Grasping for Target-driven Object Manipulation in Clutter

Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Chris Paxton, Dieter Fox
ICRA 2020poster

Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulator. Most existing data-driven approaches avoid this problem by limiting themselves to top-down planar grasps which is ins…

Cited by 263Source ↗code ↗Scholar ↗
2020

Self-supervised 6D Object Pose Estimation for Robot Manipulation

Xinke Deng, Yu Xiang, Arsalan Mousavian, Clemens Eppner, Timothy Bretl, Dieter Fox
ICRA 2020poster

To teach robots skills, it is crucial to obtain data with supervision. Since annotating real world data is time-consuming and expensive, enabling robots to learn in a self- supervised way is important. In this work, we introduce a robot system for self-supervised 6D object pose estimation. Starting…

Cited by 239Source ↗Scholar ↗
2019

Representing Robot Task Plans as Robust Logical-Dynamical Systems

Chris Paxton, Nathan Ratliff, Clemens Eppner, Dieter Fox
IROS 2019poster

It is difficult to create robust, reusable, and reactive behaviors for robots that can be easily extended and combined. Frameworks such as Behavior Trees are flexible but difficult to characterize, especially when designing reactions and recovery behaviors to consistently converge to a desired goal…

Cited by 82Source ↗Scholar ↗
2018

Physics-Based Selection of Informative Actions for Interactive Perception

Clemens Eppner, Roberto Martín-Martín, Oliver Brock
ICRA 2018poster

Interactive perception exploits the correlation between forceful interactions and changes in the observed signals to extract task-relevant information from the sensor stream. Finding the most informative interactions to perceive complex objects, like articulated mechanisms, is challenging because th…

Cited by 9Source ↗Scholar ↗
2017

Interleaving motion in contact and in free space for planning under uncertainty

Arne Sieverling, Clemens Eppner, Felix Wolff, Oliver Brock
IROS 2017poster

In this paper we present a planner that interleaves free-space motion with motion in contact to reduce uncertainty. The planner finds such motions by growing a search tree in the combined space of collision-free and contact configurations. The planner reasons efficiently about the accumulated uncert…

Cited by 28Source ↗Scholar ↗
2017

Visual detection of opportunities to exploit contact in grasping using contextual multi-armed bandits

Clemens Eppner, Oliver Brock
IROS 2017poster

Environment-constrained grasping exploits beneficial interactions between hand, object, and environment to increase grasp success. Instead of focusing on the final static relationship between hand posture and object pose, this view of grasping emphasizes the need and the opportunity to select the mo…

Cited by 21Source ↗Scholar ↗
2016

Combining model-based policy search with online model learning for control of physical humanoids

Igor Mordatch, Nikhil Mishra, Clemens Eppner, Pieter Abbeel
ICRA 2016poster

We present an automatic method for interactive control of physical humanoid robots based on high-level tasks that does not require manual specification of motion trajectories or specially-designed control policies. The method is based on the combination of a model-based policy that is trained off-li…

Cited by 68Source ↗Scholar ↗
2016

Learning dexterous manipulation for a soft robotic hand from human demonstrations

Abhishek Gupta, Clemens Eppner, Sergey Levine, Pieter Abbeel
IROS 2016poster

Dexterous multi-fingered hands can accomplish fine manipulation behaviors that are infeasible with simple robotic grippers. However, sophisticated multi-fingered hands are often expensive and fragile. Low-cost soft hands offer an appealing alternative to more conventional devices, but present consid…

Cited by 228Source ↗Scholar ↗
2016

Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems

Clemens Eppner, Sebastian Höfer, Rico Jonschkowski, Roberto Martín-Martín, Arne Sieverling, Vincent Wall +1
RSS 2016poster

We describe the winning entry to the Amazon Picking Challenge. From the experience of building this system and competing in the Amazon Picking Challenge, we derive several conclusions: 1) We suggest to characterize robotic systems building along four key aspects, each of them spanning a spectrum of…

Cited by 280Source ↗PDF ↗Scholar ↗
2016

Probabilistic multi-class segmentation for the Amazon Picking Challenge

Rico Jonschkowski, Clemens Eppner, Sebastian Höfer, Roberto Martín-Martín, Oliver Brock
IROS 2016poster

We present a method for multi-class segmentation from RGB-D data in a realistic warehouse picking setting. The method computes pixel-wise probabilities and combines them to find a coherent object segmentation. It reliably segments objects in cluttered scenarios, even when objects are translucent, re…

Cited by 81Source ↗Scholar ↗
2015

A taxonomy of human grasping behavior suitable for transfer to robotic hands

Fabian Heinemann, Steffen Puhlmann, Clemens Eppner, José Élvarez-Ruiz, Marianne Maertens, Oliver Brock
ICRA 2015poster

As a first step towards transferring human grasping capabilities to robots, we analyzed the grasping behavior of human subjects. We derived a taxonomy in order to adequately represent the observed strategies. During the analysis of the recorded data, this classification scheme helped us to obtain a…

Cited by 37Source ↗Scholar ↗