Achieving adaptive, multi-skilled, and energy-efficient locomotion is vital for advancing the operation of autonomous quadrupedal systems. This study presents eGAIT, a unified multi-skilled policy enabling energy-efficient and stable gait transitions across nine non-monotonic, velocity-optimized gaits, in response to dynamic velocity commands. The framework leverages a hybrid control architecture that integrates model-based and learning-based methods to address the entire locomotion pipeline. An MPC-based gait generator produces velocity-optimized trajectories, which are imitated through Proximal Policy Optimization (PPO), driven by a Adversarial Motion Prior (AMP) style reward to train distinct policies for specific velocity ranges. These policies are unified through a Hierarchical Reinforcement Learning (HRL) framework featuring a novel modified Deep Q-Network (eDQN) for real-time velocity-to-policy mapping. Training efficiency is enhanced by an auxiliary selector layer that guides velocity-policy mapping, while a sparsely activated stability reward mechanism ensures smooth gait transitions by incorporating geometric and rotational stability. Extensively validated in simulation and on a Unitree Go1 robot, eGAIT achieves a 100% success rate in velocity-to-policy mapping, a 35% improvement in energy efficiency, a 31% improvement in both velocity tracking and stability compared to the next best state-of-the-art method. This work advances autonomous quadrupedal locomotion, enabling longer, more efficient, and stable operations in dynamic environments. Supplementary materials and visualizations related to the paper can be found at: https://github.com/RPL-CS-UCL/egait/.

Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments. However, some challenges remain: the limited scene representation capability of implicit encoding, the uncertainty in the rendering process from implicit representations, and the disruption of consistency by dynamic objects. To address these challenges, we propose a dynamic visual SLAM system based on local-global fusion neural implicit representation, named DVN-SLAM. To improve the scene representation capability, we introduce a local-global fusion neural implicit representation that enables the construction of an implicit map while considering both global structure and local details. To tackle uncertainties arising from the rendering process, we design an information concentration loss for optimization, aiming to concentrate scene information on object surfaces. The proposed DVN-SLAM achieves competitive performance in localization and mapping across multiple datasets. More importantly, DVN-SLAM demonstrates robustness without semantic and optical flow prior in dynamic scenes, which sets it apart from other NeRF-based methods.

In this paper, we propose a transformer-based architecture for predicting contact forces during a physical human-robot interaction. Our Neural Network is composed of two main parts: a Multi-Layer Perceptron called Transducer and a Transformer. The former estimates, based on the kinematic data from a motion capture suit, the current contact forces. The latter predicts – taking as input the same kinematic data and the output of the Transducer – the human motions and the contact forces over a time window in the future. We validated our approach by testing the network on directions of motions that were not provided in the training set. We also compared our approach to a purely Transformer-based network, showing a better prediction accuracy of the contact forces.

Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addressed. In this paper, we introduce the use of Sample-Based Stochastic control strategies for quadrupedal robots, as an alternative to traditional optimal control laws. We show that Sample-Based Stochastic methods, supported by GPU acceleration, can be effectively applied to real quadruped robots. In particular, in this work, we focus on achieving gait frequency adaptation, a notable challenge in quadrupedal locomotion for gradient-based methods. To validate the effectiveness of Sample-Based Stochastic controllers we test two distinct approaches for quadrupedal robots and compare them against a conventional gradientbased Model Predictive Control system. Our findings, validated both in simulation and on a real 21Kg Aliengo quadruped, demonstrate that our method is on par with a traditional Model Predictive Control strategy when the robot is subject to zero or moderate disturbance, while it surpasses gradient-based methods in handling sustained external disturbances, thanks to the straightforward gait adaptation strategy that is possible to achieve within their formulation.

In this paper, we introduce a method to tackle the problem of robot local path planning among pushable objects –an open problem in robotics. In particular, we simultaneously train multiple agents in a physics-based simulation environment, utilizing an Advantage Actor-Critic algorithm coupled with a deep neural network. The developed online policy enables these agents to push obstacles in ways that are not limited to axial alignments, adapt to unforeseen changes in obstacle dynamics instantaneously, and effectively tackle local path planning in confined areas. We tested the method in various simulated environments to prove the adaptation effectiveness to various unseen scenarios in unfamiliar settings. Moreover, we have successfully applied this policy on an actual quadruped robot, confirming its capability to handle the unpredictability and noise associated with real-world sensors and the inherent uncertainties in unexplored object-pushing tasks.

We present an extension of our previously proposed IS-MPC method for humanoid gait generation aimed at obtaining robust performance in the presence of disturbances. The considered disturbance signals vary in a range of known amplitude around a mid-range value that can change at each sampling time, but whose current value is assumed to be available. The method consists in modifying the stability constraint that is at the core of IS-MPC by incorporating the current mid-range disturbance, and performing an appropriate restriction of the ZMP constraint in the control horizon on the basis of the range amplitude of the disturbance. We derive explicit conditions for recursive feasibility and internal stability of the IS-MPC method with constraint modification. Finally, we illustrate its superior performance with respect to the nominal version by performing dynamic simulations on the NAO robot.

In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety and accuracy of the surgical task. Mechanical designs and actuation schemes have inevitable limitations such as backlash and joint limits. Moreover, surgical robots need to operate in narrow pathways, which may give rise to additional environmental constraints. Therefore, the control strategies must be capable of satisfying the desired motion trajectories and the imposed constraints. Model Predictive Control (MPC) has proven effective for this purpose, allowing to solve an optimal problem by taking into consideration the evolution of the system states, cost function, and constraints over time. The high nonlinearities in tendon-driven systems, adopted in many surgical robots, are difficult to be modelled analytically. In this work, we use a model learning approach for the dynamics of tendon-driven robots. The dynamic model is then employed to impose constraints on the torques of the robot under consideration and solve an optimal constrained control problem for trajectory tracking by using MPC. To assess the capabilities of the proposed framework, both simulated and real world experiments have been conducted.

One of the key problems in planning and control of redundant robots is the fast generation of controls when multiple tasks and constraints need to be satisfied. In the literature, this problem is classically solved by multi-task prioritized approaches, where the priority of each task is determined by a weight function, describing the task strict/soft priority. In this paper, we propose to leverage machine learning techniques to learn the temporal profiles of the task priorities, represented as parametrized weight functions: we automatically determine their parameters through a stochastic optimization procedure. We show the effectiveness of the proposed method on a simulated 7 DOF Kuka LWR and both a simulated and a real Kinova Jaco arm. We compare the performance of our approach to a state-of-the-art method based on soft task prioritization, where the task weights are typically hand-tuned.