Publications by Sylvain Calinon

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We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a Programming by Demonstration (PbD) framework and for generalizing the acquired knowledge to various situations. In previous work, we pr ...

Generating intelligent robot behavior in contact-rich settings is a research problem where zeroth-order methods currently prevail. A major contributor to the success of such methods is their robustness in the face of non-smooth and discontinuous optimization landscapes that are characteristic of contact interactions, yet zeroth-order methods remain computationally inefficient. It is therefore desirable to develop methods for perception, planning and control in contact-rich settings that can achieve further

The computation of the forward dynamics plays an important role in simulating the motion of interconnected rigid bodies while considering the physical properties and constraints of each part. The applications in robotics, graphics and animation usually require fast computation, which leads to the usage of fast recursive algorithms. In this paper, we present a formulation of the recursive forward dynamics of serial kinematic chains that is rooted in geometry, which allows coordinate-free view and geometrical

Geometry is a fundamental part of robotics and there have been various frameworks of representation over the years. Recently, geometric algebra has gained attention for its property of unifying many of those previous ideas into one algebra. While there are already efficient open-source implementations of geometric algebra available, none of them is targeted at robotics applications. We want to address this shortcoming with our library gafro. This article presents an overview of the implementation details as

Imitation Learning offers a promising approach to learn directly from data without requiring explicit models, simulations, or detailed task definitions. During inference, actions are sampled from the learned distribution and executed on the robot. However, sampled actions may fail for various reasons, and simply repeating the sampling step until a successful action is obtained can be inefficient. In this work, we propose an enhanced sampling strategy that refines the sampling distribution to avoid previousl

We present a Programming by Demonstration (PbD) framework for generically extracting the relevant features of a given task and for addressing the problem of generalizing the acquired knowledge to different contexts. We validate the architecture through a s ...

paper studies exploiting action-level learning (imitation) in the optimal control problem context. Cost functions defined by the optimal control methods are similar to the goal-level learning (emulation) in animals. However, imitating the robot's or others ...

Generating motions for robots interacting with objects of various shapes is a complex challenge, further complicated by the robot’s geometry and multiple desired behaviors. While current robot programming tools (such as inverse kinematics, collision avoidance, and manipulation planning) often treat these problems as constrained optimization, many existing solvers focus on specific problem domains or do not exploit geometric constraints effectively. We propose an efficient first-order method, Augmented Lagra

This paper presents a method by which a robot can learn through observation to perform a collaborative manipulation task, namely lifting an object. The task is first demonstrated by a user controlling the robot's hand via a haptic interface. Learning extra ...

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

To fulfill a need for natural, user-friendly means of interacting and reprogramming toy and humanoid robots, a growing trend of robotics research investigates the integration of methods for gesture recognition and natural speech processing. Unfortunately, efficient methods for speech and vision processing remain computationally expensive and, thus, cannot be easily exploited on cost- and size-limited platforms. Personal Digital Assistants (PDAs) are ideal low-cost platforms to provide simple speech and visi

Humans use tools to complete impact-aware tasks, such as hammering a nail or playing tennis. The postures adopted to use these tools can significantly influence the performance of these tasks, where the force or velocity of the hand holding a tool plays a ...

In this work, we propose a novel approach to represent robot geometry as distance fields (RDF) that extends the principle of signed distance fields (SDFs) to articulated kinematic chains. Our method employs a combination of Bernstein polynomials to encode ...

In this work, we are presenting an extension of the cooperative dual-task space (CDTS) in conformal geometric algebra. The CDTS was first defined using dual quaternion algebra and is a well established framework for the simplified definition of tasks using ...

We propose a task-space admittance controller for dual-arm robotic systems using conformal geometric algebra. The controller is a reinterpretation of a previous work using dual quaternion algebra. By introducing conformal geometric algebra, we aim to enhance the geometric expressiveness, which simplifies the modeling of various tasks and opens doors to more complex applications, such as the modeling of multiple points of contact on the robotic arm in a whole-body manipulation task. We first show the derivat

Contact-rich manipulation plays an important role in daily human activities. However, uncertain physical parameters often pose significant challenges for both planning and control. A promising strategy is to develop policies that are robust across a wide r ...

Large image generation and vision models, combined with differentiable rendering technologies, have become powerful tools for generating paths that can be drawn or painted by a robot. However, these tools often overlook the intrinsic physicality of the human drawing/writing act, which is usually executed with skillful hand/arm gestures. Taking this into account is important for the visual aesthetics of the results and for the development of closer and more intuitive artist-robot collaboration scenarios. We

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are oft ...

Robot Programming by Demonstration (RbD) covers methods by which a robot learns new skills through human guidance. In this work, we take the perspective that the role of the teacher is more important than just being a model of successful behaviour, and pre ...

We present an efficient task and motion replanning approach for sequential multi-object manipulation in dynamic environments. Conventional Task And Motion Planning (TAMP) solvers experience an exponential increase in planning time as the planning horizon and number of objects grow, limiting their applicability in real-world scenarios. To address this, we propose learning problem decompositions from demonstrations to accelerate TAMP solvers. Our approach consists of three key components: goal decomposition l

Sylvain Calinon

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Statistical Learning by Imitation of Competing Constraints in Joint Space and Task Space

A Smooth Analytical Formulation of Collision Detection and Rigid Body Dynamics With Contact

Recursive Forward Dynamics for Serial Kinematic Chains Using Conformal Geometric Algebra

GAFRO: Geometric Algebra for Robotics [Tutorial]

CCDP: Composition of Conditional Diffusion Policies with Guided Sampling

On Learning, Representing and Generalizing a Task in a Humanoid Robot

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Efficient and Real-Time Motion Planning for Robotics Using Projection-Based Optimization

Learning collaborative manipulation tasks by demonstration using a haptic interface

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

PDA Interface for Humanoid Robots

An Optimal Control Formulation of Tool Affordance Applied to Impact Tasks

Representing Robot Geometry as Distance Fields: Applications to Whole-body Manipulation

Extending the Cooperative Dual-Task Space in Conformal Geometric Algebra

Dual-arm admittance control using conformal geometric algebra

Robust contact-rich manipulation through implicit motor adaptation

Image-driven Robot Drawing with Rapid Lognormal Movements

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Active Teaching in Robot Programming by Demonstration

Learning Problem Decomposition for Efficient Sequential Multi-object Manipulation Planning