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Kostas E. Bekris

34 accepted papers

2026

An Open-Source, Reproducible Tensegrity Robot That Can Navigate Among Obstacles

William R. Johnson III, Patrick Meng, Nelson Chen, Luca Cimatti, Augustin Vercoutere, Mridul Aanjaneya +2
RA-L 2026

Tensegrity robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, coupled dynamics, however, present modeling and control challenges, hindering planning and obstacle avoidance. This letter pre

Cited by 3Source ↗Scholar ↗
2025

Integrating Model-Based Control and RL for Sim2Real Transfer of Tight Insertion Policies

Isidoros Marougkas, Dhruv Metha Ramesh, Joe Doerr, Edgar Granados, Aravind Sivaramakrishnan, Abdeslam Boularias +1
ICRA 2025

Object insertion under tight tolerances (<Imm) is an important but challenging assembly task as even small errors can result in undesirable contacts. Recent efforts focused on Reinforcement Learning (RL), which often depends on careful definition of dense reward functions. This work proposes an effe

Cited by 5Source ↗Scholar ↗
2025

PROBE: Proprioceptive Obstacle Detection and Estimation while Navigating in Clutter

Dhruv Metha Ramesh, Aravind Sivaramakrishnan, Shreesh Keskar, Kostas E. Bekris, Jingjin Yu, Abdeslam Boularias
ICRA 2025

In critical applications, including search-and-rescue in degraded environments, blockages can be prevalent and prevent the effective deployment of certain sensing modalities, particularly vision, due to occlusion and the constrained range of view of onboard camera sensors. To enable robots to tackle

Cited by 0Source ↗code ↗Scholar ↗
2024

MORALS: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space

Ewerton R. Vieira, Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados, Konstantin Mischaikow, Kostas E. Bekris
ICRA 2024poster

Estimating the region of attraction (RoA) for a robot controller is essential for safe application and controller composition. Many existing methods require a closed-form expression that limit applicability to data-driven controllers. Methods that operate only over trajectory rollouts tend to be dat…

Cited by 3Source ↗Scholar ↗
2024

Roadmaps with Gaps over Controllers: Achieving Efficiency in Planning under Dynamics

Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados, Noah R. Carver, Kostas E. Bekris
IROS 2024poster

This paper aims to improve the computational efficiency of motion planning for mobile robots with non-trivial dynamics through the use of learned controllers. Offline, a system-specific controller is first trained in an empty environment. Then, for the target environment, the approach constructs a d…

Cited by 3Source ↗code ↗Scholar ↗
2023

Corrections to "Probabilistic Completeness of RRT for Geometric and Kinodynamic Planning With Forward Propagation"

Michal Kleinbort, Kiril Solovey, Zakary Littlefield, Kostas E. Bekris, Dan Halperin
RA-L 2023

Our original publication Kleinbort et al. (2019) contains an error in the analysis of the case of the kinodynamic RRT. Here, we rectify the problem by modifying the proof of Theorem <xref ref-type="theorem" rid="theorem2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/

Cited by 2Source ↗Scholar ↗
2023

Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees

Ewerton R. Vieira, Aravind Sivaramakrishnan, Yao Song, Edgar Granados, Marcio Gameiro, Konstantin Mischaikow +2
ICRA 2023poster

This paper proposes an integration of surrogate modeling and topology to significantly reduce the amount of data required to describe the underlying global dynamics of robot controllers, including closed-box ones. A Gaussian Process (GP), trained with randomized short trajectories over the state-spa…

Cited by 4Source ↗Scholar ↗
2023

Resolution Complete In-Place Object Retrieval given Known Object Models

Daniel Nakhimovich, Yinglong Miao, Kostas E. Bekris
ICRA 2023poster

This work proposes a robot task planning framework for retrieving a target object in a confined workspace among multiple stacked objects that obstruct the target. The robot can use prehensile picking and in-workspace placing actions. The method assumes access to 3D models for the visible objects in…

Cited by 0Source ↗Scholar ↗
2023

StarBlocks: Soft Actuated Self-Connecting Blocks for Building Deformable Lattice Structures

Luyang Zhao, Yijia Wu, Wenzhong Yan, Weishu Zhan, Xiaonan Huang, Joran W. Booth +4
RA-L 2023

In this paper, we present a soft modular block inspired by tensegrity structures that can form load-bearing structures through self-assembly. The block comprises a stellated compliant skeleton, shape memory alloy muscles, and permanent magnet connectors. We classify five deformation primitives for i

Cited by 30Source ↗Scholar ↗
2022

Complex In-Hand Manipulation Via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

Andrew S. Morgan, Kaiyu Hang, Bowen Wen, Kostas E. Bekris, Aaron M. Dollar
RA-L 2022

Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand’s kinematic topology. Finger gaiting is one way to alleviate such restraints. It allows contacts to be freely broken and remade so as to operate on different m

Cited by 71Source ↗Scholar ↗
2022

Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

Rui Wang, Yinglong Miao, Kostas E. Bekris
ICRA 2022poster

Prehensile object rearrangement in cluttered and confined spaces has broad applications but is also challenging. For instance, rearranging products in a grocery shelf means that the robot cannot directly access all objects and has limited free space. This is harder than tabletop rearrangement where…

Cited by 37Source ↗code ↗Scholar ↗
2022

Fast High-Quality Tabletop Rearrangement in Bounded Workspace

Kai Gao, Darren Lau, Baichuan Huang, Kostas E. Bekris, Jingjin Yu
ICRA 2022poster

In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a common task that we solve on a daily basis, are essential in enabling truly intelligent robotic manipulation. In a given…

Cited by 36Source ↗code ↗Scholar ↗
2022

Persistent Homology for Effective Non-Prehensile Manipulation

Ewerton R. Vieira, Daniel Nakhimovich, Kai Gao, Rui Wang, Jingjin Yu, Kostas E. Bekris
ICRA 2022poster

This work explores the use of topological tools for achieving effective non-prehensile manipulation in cluttered, constrained workspaces. In particular, it proposes the use of persistent homology as a guiding principle in identifying the appropriate non-prehensile actions, such as pushing, to clean…

Cited by 27Source ↗Scholar ↗
2022

Terrain-Aware Learned Controllers for Sampling-Based Kinodynamic Planning over Physically Simulated Terrains

Troy McMahon, Aravind Sivaramakrishnan, Kushal Kedia, Edgar Granados, Kostas E. Bekris
IROS 2022poster

This paper explores learning an effective controller for improving the efficiency of kinodynamic planning for vehicular systems navigating uneven terrains. It describes the pipeline for training the corresponding controller and using it for motion planning purposes. The training process uses a soft…

Cited by 7Source ↗Scholar ↗
2021

Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers

Aravind Sivaramakrishnan, Edgar Granados, Seth Karten, Troy McMahon, Kostas E. Bekris
IROS 2021poster

This paper aims to improve the path quality and computational efficiency of sampling-based kinodynamic planners for vehicular navigation. It proposes a learning framework for identifying promising controls during the expansion process of sampling-based planners. Given a dynamics model, a reinforceme…

Cited by 11Source ↗Scholar ↗
2021

Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search

Rui Wang, Kai Gao, Daniel Nakhimovich, Jingjin Yu, Kostas E. Bekris
ICRA 2021poster

Object rearrangement is a widely-applicable and challenging task for robots. Geometric constraints must be carefully examined to avoid collisions and combinatorial issues arise as the number of objects increases. This work studies the algorithmic structure of rearranging uniform objects, where robot…

Cited by 47Source ↗Scholar ↗
2020

Motion Planning with Competency-Aware Transition Models for Underactuated Adaptive Hands

Avishai Sintov, Andrew Kimmel, Kostas E. Bekris, Abdeslam Boularias
ICRA 2020poster

Underactuated adaptive hands simplify grasping tasks but it is difficult to model their interactions with objects during in-hand manipulation. Learned data-driven models have been recently shown to be efficient in motion planning and control of such hands. Still, the accuracy of the models is limite…

Cited by 10Source ↗Scholar ↗
2020

Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space

Michal Kleinbort, Edgar Granados, Kiril Solovey, Riccardo Bonalli, Kostas E. Bekris, Dan Halperin
ICRA 2020poster

We present a novel analysis of AO-RRT: a tree-based planner for motion planning with kinodynamic constraints, originally described by Hauser and Zhou (AO-X, 2016). AO-RRT explores the state-cost space and has been shown to efficiently obtain high-quality solutions in practice without relying on the…

Cited by 34Source ↗Scholar ↗
2020

Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands

Bowen Wen, Chaitanya Mitash, Sruthi Soorian, Andrew Kimmel, Avishai Sintov, Kostas E. Bekris
ICRA 2020poster

Many manipulation tasks, such as placement or within-hand manipulation, require the object's pose relative to a robot hand. The task is difficult when the hand significantly occludes the object. It is especially hard for adaptive hands, for which it is not easy to detect the finger's configuration.…

Cited by 50Source ↗code ↗Scholar ↗
2020

Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses

Rui Wang, Chaitanya Mitash, Shiyang Lu, Daniel Boehm, Kostas E. Bekris
IROS 2020poster

Picking an item in the presence of other objects can be challenging as it involves occlusions and partial views. Given object models, one approach is to perform object pose estimation and use the most likely candidate pose per object to pick the target without collisions. This approach, however, ign…

Cited by 9Source ↗Scholar ↗
2020

Task-Driven Perception and Manipulation for Constrained Placement of Unknown Objects

Chaitanya Mitash, Rahul Shome, Bowen Wen, Abdeslam Boularias, Kostas E. Bekris
RA-L 2020

Recent progress in robotic manipulation has dealt with the case of previously unknown objects in the context of relatively simple tasks, such as bin-picking. Existing methods for more constrained problems, however, such as deliberate placement in a tight region, depend more critically on shape infor

Cited by 43Source ↗Scholar ↗
2020

se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains

Bowen Wen, Chaitanya Mitash, Baozhang Ren, Kostas E. Bekris
IROS 2020poster

Tracking the 6D pose of objects in video sequences is important for robot manipulation. This task, however, introduces multiple challenges: (i) robot manipulation involves significant occlusions; (ii) data and annotations are troublesome and difficult to collect for 6D poses, which complicates machi…

Cited by 142Source ↗code ↗Scholar ↗
2019

Learning a State Transition Model of an Underactuated Adaptive Hand

Avishai Sintov, Andrew S. Morgan, Andrew Kimmel, Aaron M. Dollar, Kostas E. Bekris, Abdeslam Boularias
RA-L 2019

Fully actuated multifingered robotic hands are often expensive and fragile. Low-cost underactuated hands are appealing but present challenges due to the lack of analytical models. This letter aims to learn a stochastic version of such models automatically from data with minimum user effort. The focu

Cited by 32Source ↗Scholar ↗
2019

Probabilistic Completeness of RRT for Geometric and Kinodynamic Planning With Forward Propagation

Michal Kleinbort, Kiril Solovey, Zakary Littlefield, Kostas E. Bekris, Dan Halperin
RA-L 2019

The rapidly exploring random tree (RRT) algorithm has been one of the most prevalent and popular motion-planning techniques for two decades now. Surprisingly, in spite of its centrality, there has been an active debate under which conditions RRT is probabilistically complete. We provide two new proo

Cited by 90Source ↗Scholar ↗
2019

Towards Robust Product Packing with a Minimalistic End-Effector

Rahul Shome, Wei N. Tang, Changkyu Song, Chaitanya Mitash, Hristiyan Kourtev, Jingjin Yu +2
ICRA 2019poster

Advances in sensor technologies, object detection algorithms, planning frameworks and hardware designs have motivated the deployment of robots in warehouse automation. A variety of such applications, like order fulfillment or packing tasks, require picking objects from unstructured piles and careful…

Cited by 67Source ↗Scholar ↗
2018

Efficient and Asymptotically Optimal Kinodynamic Motion Planning via Dominance-Informed Regions

Zakary Littlefield, Kostas E. Bekris
IROS 2018poster

Motion planners have been recently developed that provide path quality guarantees for robots with dynamics. This work aims to improve upon their efficiency, while maintaining their properties. Inspired by informed search principles, one objective is to use heuristics. Nevertheless, comprehensive and…

Cited by 54Source ↗Scholar ↗
2018

Improving 6D Pose Estimation of Objects in Clutter Via Physics-Aware Monte Carlo Tree Search

Chaitanya Mitash, Abdeslam Boularias, Kostas E. Bekris
ICRA 2018poster

This work proposes a process for efficiently searching over combinations of individual object 6D pose hypotheses in cluttered scenes, especially in cases involving occlusions and objects resting on each other. The initial set of candidate object poses is generated from state-of-the-art object detect…

Cited by 50Source ↗Scholar ↗
2017

A self-supervised learning system for object detection using physics simulation and multi-view pose estimation

Chaitanya Mitash, Kostas E. Bekris, Abdeslam Boularias
IROS 2017poster

Progress has been achieved recently in object detection given advancements in deep learning. Nevertheless, such tools typically require a large amount of training data and significant manual effort to label objects. This limits their applicability in robotics, where solutions must scale to a large n…

Cited by 143Source ↗Scholar ↗
2016

A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place

Colin Rennie, Rahul Shome, Kostas E. Bekris, Alberto F. De Souza
RA-L 2016

An important logistics application of robotics involves manipulators that pick-and-place objects placed in warehouse shelves. A critical aspect of this task corresponds to detecting the pose of a known object in the shelf using visual data. Solving this problem can be assisted by the use of an RGBD

Cited by 217Source ↗Scholar ↗
2016

Efficiently solving general rearrangement tasks: A fast extension primitive for an incremental sampling-based planner

Athanasios Krontiris, Kostas E. Bekris
ICRA 2016

Manipulating multiple movable obstacles is a hard problem that involves searching high-dimensional C-spaces. A milestone method for this problem was able to compute solutions for monotone instances. These are problems where every object needs to be transferred at most once to achieve a desired arran

Cited by 76Source ↗Scholar ↗
2015

Geometric probability results for bounding path quality in sampling-based roadmaps after finite computation

Andrew Dobson, George V. Moustakides, Kostas E. Bekris
ICRA 2015poster

Sampling-based algorithms provide efficient solutions to high-dimensional, geometrically complex motion planning problems. For these methods asymptotic results are known in terms of completeness and optimality. Previous work by the authors argued that such methods also provide probabilistic near-opt…

Cited by 20Source ↗Scholar ↗