65 accepted papers
Enabling legged robots to perform non-prehensile loco-manipulation is crucial for enhancing their versatility. However, learning behaviors such as whole-body object pushing often necessitates sophisticated planning strategies or extensive task-specific reward shaping. In this work, we present CAIMAN…
Data-driven methods have shown great potential in solving challenging manipulation tasks; however, their application in the domain of deformable objects has been constrained, in part, by the lack of data. To address this lack, we propose PokeFlex, a dataset featuring real-world multimodal data that …
Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both precise end-effector control and robustness to unmodeled dynamics. While model-based controllers provide precise planning …
Robotic manipulation is a fundamental challenge in the pursuit of automating household tasks and advancing robotic manufacturing. Manipulation planning for deformable objects is particularly challenging due to the associated large action spaces and complex dynamics. For deformable objects, some acti
Safe exploration is a key requirement for reinforcement learning agents to learn and adapt online, beyond controlled (e.g. simulated) environments. In this work, we tackle this challenge by utilizing suboptimal yet conservative policies (e.g., obtained from offline data or simulators) as priors. Our…
This work presents a motion retargeting approach for legged robots, aimed at transferring the dynamic and agile movements to robots from source motions. In particular, we guide the imitation learning procedures by transferring motions from source to target, effectively bridging the morphological dis…
Loco-manipulation demands coordinated whole-body motion to manipulate objects effectively while maintaining locomotion stability, presenting significant challenges for both planning and control. In this work, we propose a whole-body model predictive control (MPC) framework that directly optimizes jo
Loco-manipulation demands coordinated whole-body motion to manipulate objects effectively while maintaining locomotion stability, presenting significant challenges for both planning and control. In this work, we propose a whole-body model predictive control (MPC) framework that directly optimizes jo…
Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with their environments to learn effectively. These limitations confine RL agents to simulated environments, hindering their…
Robotic systems are routinely used in the logistics industry to enhance operational efficiency, but the design of robot workspaces remains a complex and manual task, which limits the system’s flexibility to changing demands. This paper aims to automate robot workspace design by proposing a computati
We present a novel differentiable approach to quantifying and optimizing stability in robotic systems addressing an open challenge in the field of robot analysis, control, design, and optimization. Our method leverages differentiable simulation over extended time horizons to estimate a robustness me
Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected exploration, i.e., select random sequences of actions. Exploration can also be directed using intrinsic re…
Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both precise end-effector control and robustness to unmodeled dynamics. While model-based controllers provide precise planning
Deploying reinforcement learning (RL) safely in the real world is challenging, as policies trained in simulators must face the inevitable *sim-to-real gap*. Robust safe RL techniques are provably safe, however difficult to scale, while domain randomization is more practical yet prone to unsafe behav…
Robotic manipulation of deformable objects remains challenging due to the high dimensional configuration space and complex dynamics. In this work we demonstrate how the abstraction level used for modeling deformable objects can significantly impact the difficulty of the motion planning problem. We s
We present Sim-FSVGD for learning robot dynamics from data. As opposed to traditional methods, Sim-FSVGD leverages low-fidelity physical priors, e.g., in the form of simulators, to regularize the training of neural network models. While learning accurate dynamics already in the low data regime, Sim-…
We introduce PACOH-RL, a novel model-based Meta-Reinforcement Learning (Meta-RL) algorithm designed to efficiently adapt control policies to changing dynamics. PACOH-RL meta-learns priors for the dynamics model, allowing swift adaptation to new dynamics with minimal interaction data. Existing Meta-R
Control policies trained using deep reinforcement learning often generate stiff, high-frequency motions in response to unexpected disturbances. To promote more natural and compliant balance recovery strategies, we propose a simple modification to the typical reinforcement learning training process.…
We study the problem of nonepisodic reinforcement learning (RL) for nonlinear dynamical systems, where the system dynamics are unknown and the RL agent has to learn from a single trajectory, i.e., without resets. We propose **N**on**e**pisodic **O**ptistmic **RL** (NeoRL), an approach based on the p…
We propose a self-supervised approach for learning physics-based subspaces for real-time simulation. Existing learning-based methods construct subspaces by approximating pre-defined simulation data in a purely geometric way. However this approach tends to produce high-energy configurations leads to…
Cooperative mobile manipulation is an increasingly important topic in robotics: Just as humans need to collaborate on many tasks, robots need to be able to work together, e.g., to transport heavy or unwieldy objects in unstructured environments. But mobile multi-robot systems pose unique challenges,
Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neural networks have demonstrated empirical and qualitative robustness against real-world uncertainties, including sensor no…
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level objectives are specified as a variable number of partial or complete pose targets that are spaced arbitrarily in time. Our…
Precise reconstruction and manipulation of the crumpled cloths is challenging due to the high dimensionality of cloth models, as well as the limited observation at self-occluded regions. We leverage the recent progress in the field of single-view reconstruction to template-based reconstruct the crum…
In this work, we extend the model predictive control methods developed in the legged robotics literature to collaborative loco-manipulation settings. The systems we study entail a payload collectively carried by multiple quadruped robots equipped with a mechanical arm. We use a direct multiple shoot…
We propose a computational approach for designing fully-integrated compliant mechanisms with bio-inspired joints that are stabilized and actuated by elastic elements. Similar to human knees or finger phalanges, our mechanisms leverage sliding between pairs of contacting surfaces to generate complex…
Task and motion planning is one of the key problems in robotics today. It is often formulated as a discrete task allocation problem combined with continuous motion planning. Many existing approaches to TAMP involve explicit descriptions of task primitives that cause discrete changes in the kinematic…
Real-world robotic manipulation tasks remain an elusive challenge, since they involve both fine-grained environment interaction, as well as the ability to plan for long-horizon goals. Although deep reinforcement learning (RL) methods have shown encouraging results when planning end-to-end in high-di…
Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate analytical models of the dynamics, yet some aspects of the physical world can only be captured to a limited extent. An alternative approach i…
Mobile robots are becoming safer and more affordable, and their presence in the workspace is increasing. However, many tasks that involve reasoning, long-term planning or human preferences are still hard to automate. While some solutions in specialised areas slowly emerge, an alternative to full aut…
Robot mechanisms that exploit compliance can perform complex tasks under uncertainty using simple control strategies, but it remains difficult to design mechanisms with a desired embodied intelligence. In this article, we propose an automated design technique that optimizes the desired load-displace
Reinforcement learning algorithms commonly seek to optimize policies for solving one particular task. How should we explore an unknown dynamical system such that the estimated model allows us to solve multiple downstream tasks in a zero-shot manner? In this paper, we address this challenge, by deve…
This letter presents a control framework that combines model-based optimal control and reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our approach enhances the RL training process by incorporating on-demand reference motions generated through finite-horizon optimal co
This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. Our approach leverages a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in t…
We present Ungar, an open-source library to aid the implementation of high-dimensional optimal control problems (OCPs). We adopt modern template metaprogramming techniques to enable the compile-time modeling of complex systems while retaining maximum runtime efficiency. Our framework provides syntac…
This work presents a motion capture-driven locomotion controller for quadrupedal robots that replicates the non-periodic footsteps and subtle body movement of animal motions. We adopt a nonlinear model predictive control (NMPC) formulation that generates optimal base trajectories and stepping locati…
A central aspect of robotic motion planning is collision avoidance, where a multitude of different approaches are currently in use. Optimization-based motion planning is one method, that often heavily relies on distance computations between robots and obstacles. These computations can easily become…
A variety of control tasks such as inverse kinematics (IK), trajectory optimization (TO), and model predictive control (MPC) are commonly formulated as energy minimization problems. Numerical solutions to such problems are well-established. However, these are often too slow to be used directly in re
Multi-Robot Systems (MRS) present many advantages over single robots, e.g. improved stability and payload capacity. Being able to operate or teleoperate these systems is therefore of high interest in industries such as construction or logistics. However, controlling the collective motion of a MRS ca…
Elastic lattice-like materials whose structures can be tuned to achieve desired mechanical properties hold great promise for many applications in robotics. However, existing methods rely on all-fused connections at lattice nodes, which limits the range of mechanical properties that can be achieved.
In this paper, we explore the challenge of generating animal-like walking motions for legged robots. To this end, we propose a versatile and robust control pipeline that combines a state-of-the-art model-based controller with a data-driven technique that is commonly used in computer animation. We de…
We present a control-aware design optimization method for quadrupedal robots. In particular, we show that it is possible to analytically differentiate typical, inverse dynamics-based whole body controllers with respect to design parameters, and that gradient-based methods can be used to efficiently…
Due to their complex dynamics and high-dimensional configuration spaces, non-rigid objects such as cables, garments, bedding and various food items remain notoriously challenging for robots to manipulate effectively. In this letter, we therefore develop, validate and analyze model-based optimal cont
We combine Boston Dynamics Spot® with a light-weight, external robot arm to perform dynamic grasping maneuvers. While Spot is a reliable, robust and easy-to-control mobile robot, these highly desirable qualities come with the price that the control access granted to the user is restricted. Consequen…
Teleoperation provides a way for human operators to guide robots in situations where full autonomy is challenging or where direct human intervention is required. It can also be an important tool to teach robots in order to achieve autonomous behaviour later on. The increased availability of collabor…
Recent advances in implicit neural representations show great promise when it comes to generating numerical solutions to partial differential equations. Compared to conventional alternatives, such representations employ parameterized neural networks to define, in a mesh-free manner, signals that are…
Current reinforcement learning (RL) methods use simulation models as simple black-box oracles. In this paper, with the goal of improving the performance exhibited by RL algorithms, we explore a systematic way of leveraging the additional information provided by an emerging class of differentiable si…
We introduce a singularity-aware design optimization method for spatial multi-degree-of-freedom mechanical linkages. At the core of our approach is an adversarial sampling strategy, which actively detects singular configurations within the targeted operation range. The detection of singularities in
Teleoperating robotic arms is a challenging task that requires years of training to master. It is mentally demanding, as the operator must internally compute transformations, or rely on muscle memory, to perform even the simplest tasks. Alternative methods that rely on embodiment –the immersive, fir…
Legged-wheeled robots promise versatile, fast and efficient mobile capabilities. To unleash their full potential, however, such hybrid robots need to be designed in a way that promotes the complex, full-body motions required for novel locomotion modes. This letter discusses the computational framewo
We present an optimization framework for grasp and motion planning in the context of robotic assembly. Typically, grasping locations are provided by higher level planners or as input parameters. In contrast, our mathematical model simultaneously optimizes motion trajectories, grasping locations, and
We consider the design of under-actuated articulated mechanism that are able to maintain stable static balance. Our method augments an user-provided design with counter-weights whose mass and attachment locations are automatically computed. The optimized counterweights adjust the center of gravity s…
We present a novel class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family which consists of ground mobile bases with non-holonomic constraints. Moreover, these mobile robots are constrained by closed-loop kinematic chains consisting of revolute joints which can be either…
We present a concept of constrained collaborative mobile agents (CCMA) system, which consists of multiple wheeled mobile agents constrained by a passive kinematic chain. This mobile robotic system is modular in nature, the passive kinematic chain can be easily replaced with different designs and mor…
Statically balanced spring mechanisms are used in many applications that support our daily lives. However, creating new designs is a challenging problem since the designer has to simultaneously determine the right number of springs, their connectivity, attachment points, and other parameters. We pro
Fabricating robots from soft materials imposes major constraints on the integration and compatibility of embedded sensing, transmission, and actuation systems. Various soft materials present different challenges, but also new opportunities, for novel fabrication techniques, integrated soft sensors,
Compliance is a defining characteristic of biological systems. Understanding how to exploit soft materials as effectively as living creatures do is consequently a fundamental challenge that is key to recreating the complex array of motor skills displayed in nature. As an important step towards this…
In this paper, we address the challenge of robotic manipulation of elastically deforming objects. To this end, we model elastic objects using the Finite Element Method. Through a quasi-static assumption, we leverage sensitivity analysis to mathematically model how changes in the robot's configuratio…
We present a computational design system that allows novices and experts alike to easily create custom robotic devices using modular electromechanical components. The core of our work consists of a design abstraction that models the way in which these components can be combined to form complex robot…
We present a class of tendon-actuated soft robots, which promise to be low-cost and accessible to non-experts. The fabrication techniques we introduce are largely based on traditional techniques for fabricating plush toys, and so we term the robots created using our approach “plush robots.” A plush…
Modular robots can be rapidly reconfigured into customized articulated legged morphologies capable of mobile manipulation and inspection. However, current gait generation methods do not keep pace with the speed of physical reconfiguration. This work focuses on quickly creating gaits for modular legg…
We present a novel computational approach to optimizing the morphological design of robotic devices. Our framework takes as input a parameterized robot design, and a motion plan consisting of end-effector trajectories and/or a body trajectory. The algorithm we propose is used to optimize a set of de…
Quadrupedal locomotion can be described as a constrained optimization problem that is very hard to solve due to the high dimensional, nonlinear and non-smooth system dynamics. In this paper, we propose a formulation that can be solved within few seconds using sequential quadratic programming. This m…
The design of legged robots is often inspired by animals evolved to excel at different tasks. However, while mimicking morphological features seen in nature can be very powerful, robots may need to perform motor tasks that their living counterparts do not. In the absence of designs that can be mimic…
Quadrupedal locomotion on sloped terrains poses different challenges than walking in a mostly flat environment. The robot's configuration needs to be explicitly controlled in order to avoid slipping and kinematic limits. To this end, information about the terrain's inclination is required for carefu…