arXiv subjects

Michele Focchi

Publications and source records attributed to Michele Focchi.

At least 19 recordsLinked to original sources

arXiv2026

Bi-Level Optimization for Contact and Motion Planning in Rope-Assisted Legged Robots

This paper presents a planning pipeline framework for locomotion in rope-assisted robots climbing vertical surfaces. The proposed framework is formulated as a bi-level optimization scheme that addresses a mixed-integer problem: selecting feasible terrain regions for landing while simultaneously optimizing the control inputs, namely rope tensions and leg forces, and landing location. The outer level of the optimization is solved using the Cross-Entropy Method, while the inner level relies on gradient-based nonlinear optimization to compute dynamically feasible motions. The approach is validated on a novel climbing robot platform, ALPINE, across a variety of challenging terrain configurations.

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arXiv2025

Guided Reinforcement Learning for Omnidirectional 3D Jumping in Quadruped Robots

Jumping poses a significant challenge for quadruped robots, despite being crucial for many operational scenarios. While optimisation methods exist for controlling such motions, they are often time-consuming and demand extensive knowledge of robot and terrain parameters, making them less robust in real-world scenarios. Reinforcement learning (RL) is emerging as a viable alternative, yet conventional end-to-end approaches lack efficiency in terms of sample complexity, requiring extensive training in simulations, and predictability of the final motion, which makes it difficult to certify the safety of the final motion. To overcome these limitations, this paper introduces a novel guided reinforcement learning approach that leverages physical intuition for efficient and explainable jumping, by combining B\'ezier curves with a Uniformly Accelerated Rectilinear Motion (UARM) model. Extensive simulation and experimental results clearly demonstrate the advantages of our approach over existing alternatives.

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arXiv2024

Pseudo-Kinematic Trajectory Control and Planning of Tracked Vehicles

Tracked vehicles distribute their weight continuously over a large surface area (the tracks). This distinctive feature makes them the preferred choice for vehicles required to traverse soft and uneven terrain. From a robotics perspective, however, this flexibility comes at a cost: the complexity of modelling the system and the resulting difficulty in designing theoretically sound navigation solutions. In this paper, we aim to bridge this gap by proposing a framework for the navigation of tracked vehicles, built upon three key pillars. The first pillar comprises two models: a simulation model and a control-oriented model. The simulation model captures the intricate terramechanics dynamics arising from soil-track interaction and is employed to develop faithful digital twins of the system across a wide range of operating conditions. The control-oriented model is pseudo-kinematic and mathematically tractable, enabling the design of efficient and theoretically robust control schemes. The second pillar is a Lyapunov-based feedback trajectory controller that provides certifiable tracking guarantees. The third pillar is a portfolio of motion planning solutions, each offering different complexity-accuracy trade-offs. The various components of the proposed approach are validated through an extensive set of simulation and experimental data.

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arXiv2024

ALPINE: a climbing robot for operations in mountain environments

Mountain slopes are perfect examples of harsh environments in which humans are required to perform difficult and dangerous operations such as removing unstable boulders, dangerous vegetation or deploying safety nets. A good replacement for human intervention can be offered by climbing robots. The different solutions existing in the literature are not up to the task for the difficulty of the requirements (navigation, heavy payloads, flexibility in the execution of the tasks). In this paper, we propose a robotic platform that can fill this gap. Our solution is based on a robot that hangs on ropes, and uses a retractable leg to jump away from the mountain walls. Our package of mechanical solutions, along with the algorithms developed for motion planning and control, delivers swift navigation on irregular and steep slopes, the possibility to overcome or travel around significant natural barriers, and the ability to carry heavy payloads and execute complex tasks. In the paper, we give a full account of our main design and algorithmic choices and show the feasibility of the solution through a large number of physically simulated scenarios.

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arXiv2023

Efficient Reinforcement Learning for Jumping Monopods

In this work, we consider the complex control problem of making a monopod reach a target with a jump. The monopod can jump in any direction and the terrain underneath its foot can be uneven. This is a template of a much larger class of problems, which are extremely challenging and computationally expensive to solve using standard optimisation-based techniques. Reinforcement Learning (RL) could be an interesting alternative, but the application of an end-to-end approach in which the controller must learn everything from scratch, is impractical. The solution advocated in this paper is to guide the learning process within an RL framework by injecting physical knowledge. This expedient brings to widespread benefits, such as a drastic reduction of the learning time, and the ability to learn and compensate for possible errors in the low-level controller executing the motion. We demonstrate the advantage of our approach with respect to both optimization-based and end-to-end RL approaches.

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arXiv2023

Reactive Landing Controller for Quadruped Robots

Quadruped robots are machines intended for challenging and harsh environments. Despite the progress in locomotion strategy, safely recovering from unexpected falls or planned drops is still an open problem. It is further made more difficult when high horizontal velocities are involved. In this work, we propose an optimization-based reactive Landing Controller that uses only proprioceptive measures for torque-controlled quadruped robots that free-fall on a flat horizontal ground, knowing neither the distance to the landing surface nor the flight time. Based on an estimate of the Center of Mass horizontal velocity, the method uses the Variable Height Springy Inverted Pendulum model for continuously recomputing the feet position while the robot is falling. In this way, the quadruped is ready to attain a successful landing in all directions, even in the presence of significant horizontal velocities. The method is demonstrated to dramatically enlarge the region of horizontal velocities that can be dealt with by a naive approach that keeps the feet still during the airborne stage. To the best of our knowledge, this is the first time that a quadruped robot can successfully recover from falls with horizontal velocities up to 3 m/s in simulation. Experiments prove that the used platform, Go1, can successfully attain a stable standing configuration from falls with various horizontal velocity and different angular perturbations.

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arXiv2023

Locosim: an Open-Source Cross-Platform Robotics Framework

The architecture of a robotics software framework tremendously influences the effort and time it takes for end users to test new concepts in a simulation environment and to control real hardware. Many years of activity in the field allowed us to sort out crucial requirements for a framework tailored for robotics: modularity and extensibility, source code reusability, feature richness, and user-friendliness. We implemented these requirements and collected best practices in Locosim, a cross-platform framework for simulation and real hardware. In this paper, we describe the architecture of Locosim and illustrate some use cases that show its potential.

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arXiv2023

An Efficient Paradigm for Feasibility Guarantees in Legged Locomotion

Developing feasible body trajectories for legged systems on arbitrary terrains is a challenging task. In this paper, we present a paradigm that allows to design feasible Center of Mass (CoM) and body trajectories in an efficient manner. In our previous work [1], we introduced the notion of the 2D feasible region, where static balance and the satisfaction of joint torque limits were guaranteed, whenever the projection of the CoM lied inside the proposed admissible region. In this work we propose a general formulation of the improved feasible region that guarantees dynamic balance alongside the satisfaction of both joint-torque and kinematic limits in an efficient manner. To incorporate the feasibility of the kinematic limits, we introduce an algorithm that computes the reachable region of the CoM. Furthermore, we propose an efficient planning strategy that utilizes the improved feasible region to design feasible CoM and body orientation trajectories. Finally, we validate the capabilities of the improved feasible region and the effectiveness of the proposed planning strategy, using simulations and experiments on the 90 kg Hydraulically actuated Quadruped (HyQ) and the 21 kg Aliengo robots.

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arXiv2023

CLIO: a Novel Robotic Solution for Exploration and Rescue Missions in Hostile Mountain Environments

Rescue missions in mountain environments are hardly achievable by standard legged robots-because of the high slopes-or by flying robots-because of limited payload capacity. We present a concept for a rope-aided climbing robot which can negotiate up-to-vertical slopes and carry heavy payloads. The robot is attached to the mountain through a rope, and it is equipped with a leg to push against the mountain and initiate jumping maneuvers. Between jumps, a hoist is used to wind/unwind the rope to move vertically and affect the lateral motion. This simple (yet effective) two-fold actuation allows the system to achieve high safety and energy efficiency. Indeed, the rope prevents the robot from falling while compensating for most of its weight, drastically reducing the effort required by the leg actuator. We also present an optimal control strategy to generate point-to-point trajectories overcoming an obstacle. We achieve fast computation time (<1 s) thanks to the use of a custom simplified robot model. We validated the generated optimal movements in Gazebo simulations with a complete robot model with a < 5% error on a 16 m long jump, showing the effectiveness of the proposed approach, and confirming the interest of our concept. Finally, we performed a reachability analysis showing that the region of achievable targets is strongly affected by the friction properties of the foot-wall contact.

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arXiv2023

Orientation Control System: Enhancing Aerial Maneuvers for Quadruped Robots

For legged robots, aerial motions are the only option to overpass obstacles that cannot be circumvent with standard locomotion gaits. In these cases, the robot must perform a leap to either jump onto the obstacle or fly over it. However, these movements represent a challenge because during the flight phase the Center of Mass (CoM) cannot be controlled, and the robot orientation has limited controllability. This paper focuses on the latter issue and proposes an Orientation Control System (OCS) consisting of two rotating and actuated masses (flywheels or reaction wheels) to gain control authority on the robot orientation. Because of the conservation of angular momentum, their rotational velocity can be adjusted to steer the robot orientation even when there are no contacts with the ground. The axes of rotation of the flywheels are designed to be incident, leading to a compact orientation control system that is capable of controlling both roll and pitch angles, considering the different moment of inertia in the two directions. We tested the concept with simulations on the robot Solo12.

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arXiv2023

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Model Predictive Control (MPC) approaches are widely used in robotics, since they guarantee feasibility and allow the computation of updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of the parameters of the cost function in order to obtain good performance. For instance, when a legged robot has to react to disturbances from the environment (e.g., recover after a push) or track a specific goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work, we propose a novel optimization-based Reference Generator which exploits a Linear Inverted Pendulum (LIP) model to compute reference trajectories for the Center of Mass while taking into account the possible under-actuation of a gait (e.g., in a trot). The obtained trajectories are used as references for the cost function of the Nonlinear MPC presented in our previous work. We also present a formulation that ensures guarantees on the response time to reach a goal without the need to tune the weights of the cost terms. In addition, footholds are corrected using the optimized reference to drive the robot towards the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.

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arXiv2022

ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion

In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic contact planner based on a multi-output regression neural network. ContactNet ranks discretized stepping regions, allowing to quickly choose the best feasible solution, even in complex environments. The low computation time, in the order of 1 ms, makes possible the execution of the contact planner concurrently with a trajectory optimizer in a Model Predictive Control (MPC) fashion. We demonstrate the effectiveness of the approach in simulation in different complex scenarios with the quadruped robot Solo12.

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arXiv2022

WoLF: the Whole-body Locomotion Framework for Quadruped Robots

The Whole-Body Locomotion Framework (WoLF) is an end-to-end software suite devoted to the loco-manipulation of quadruped robots. WoLF abstracts the complexity of planning and control of quadrupedal robot hardware into a simple to use and robust software that can be connected through multiple tele-operation devices to different quadruped robot models. Furthermore, WoLF allows controlling mounted devices, such as arms or pan-tilt cameras, jointly with the quadrupedal platform. In this short paper, we introduce the main features of WoLF and its overall software architecture.

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arXiv2022

Foothold Evaluation Criterion for Dynamic Transition Feasibility for Quadruped Robots

To traverse complex scenarios reliably a legged robot needs to move its base aided by the ground reaction forces, which can only be generated by the legs that are momentarily in contact with the ground. A proper selection of footholds is crucial for maintaining balance. In this paper, we propose a foothold evaluation criterion that considers the transition feasibility for both linear and angular dynamics to overcome complex scenarios. We devise convex and nonlinear formulations as a direct extension of the Continuous Convex Resolution of Centroidal Dynamic Trajectories (C-CROC) in a receding-horizon fashion to grant dynamic feasibility for future behaviours. The criterion is integrated with a Vision-based Foothold Adaptation (VFA) strategy that takes into account the robot kinematics, leg collisions and terrain morphology. We verify the validity of the selected footholds and the generated trajectories in simulation and experiments with the 90kg quadruped robot HyQ.

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arXiv2021

Model Predictive Control with Environment Adaptation for Legged Locomotion

Re-planning in legged locomotion is crucial to track the desired user velocity while adapting to the terrain and rejecting external disturbances. In this work, we propose and test in experiments a real-time Nonlinear Model Predictive Control (NMPC) tailored to a legged robot for achieving dynamic locomotion on a variety of terrains. We introduce a mobility-based criterion to define an NMPC cost that enhances the locomotion of quadruped robots while maximizing leg mobility and improves adaptation to the terrain features. Our NMPC is based on the real-time iteration scheme that allows us to re-plan online at $25\,\mathrm{Hz}$ with a prediction horizon of $2$ seconds. We use the single rigid body dynamic model defined in the center of mass frame in order to increase the computational efficiency. In simulations, the NMPC is tested to traverse a set of pallets of different sizes, to walk into a V-shaped chimney,and to locomote over rough terrain. In real experiments, we demonstrate the effectiveness of our NMPC with the mobility feature that allowed IIT's $87\, \mathrm{kg}$ quadruped robot HyQ to achieve an omni-directional walk on flat terrain, to traverse a static pallet, and to adapt to a repositioned pallet during a walk.

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arXiv2020

Motion Planning for Quadrupedal Locomotion: Coupled Planning, Terrain Mapping and Whole-Body Control

Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitude planner ensures the robot stability by imposing limits to the angular acceleration. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. Despite the use of a simplified model, which is restricted to flat terrain, our approach shows remarkable capability to deal with a wide range of non-coplanar terrains. The results are validated by experimental trials and comparative evaluations in a series of terrains of progressively increasing complexity.

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arXiv2020

On the Hardware Feasibility of Nonlinear Trajectory Optimization for Legged Locomotion based on a Simplified Dynamics

Simplified models are useful to increase the computational efficiency of a motion planning algorithm, but their lack of accuracy have to be managed. We propose two feasibility constraints to be included in a Single Rigid Body Dynamicsbased trajectory optimizer in order to obtain robust motions in challenging terrain. The first one finds an approximate relationship between joint-torque limits and admissible contact forces, without requiring the joint positions. The second one proposes a leg model to prevent leg collision with the environment. Such constraints have been included in a simplified nonlinear nonconvex trajectory optimization problem. We demonstrate the feasibility of the resulting motion plans both in simulation and on the Hydraulically actuated Quadruped (HyQ) robot, considering experiments on an irregular terrain.

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arXiv2020

Feasible Region: an Actuation-Aware Extension of the Support Region

In legged locomotion the projection of the robot Center of Mass (CoM) being inside the convex hull of the contact points is a commonly accepted sufficient condition to achieve static balancing. However, some of these configurations cannot be realized because the joint torques required to sustain them would be above their limits (actuation limits). In this manuscript we rule out such configurations and define the Feasible Region, a revisited support region that guarantees both global static stability in the sense of tipover and slippage avoidance and of existence of a set of joint-torques that are able to sustain the robot body weight. We show that the feasible region can be employed for the selection of feasible footholds and CoM trajectories to achieve static locomotion on rough terrains, also in presence of load intensive tasks. Key results of our approach include the efficiency in the computation of the feasible region thanks to an Iterative Projection algorithm. This allowed us to carry out successful experiments on the HyQ robot, that was able to negotiate obstacles of moderate dimensions while carrying an extra 10 kg payload.

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