Accurate physical simulation is crucial for the development and validation of control algorithms in robotic systems. Recent works in Reinforcement Learning (RL) take notably advantage of extensive simulations to produce efficient robot control. State-of-the-art servo actuator models generally fail at capturing the complex friction dynamics of these systems. This limits the transferability of simulated behaviors to real-world applications. In this work, we present extended friction models that allow to more accurately simulate servo actuator dynamics. We propose a comprehensive analysis of various friction models, present a method for identifying model parameters using recorded trajectories from a pendulum test bench, and demonstrate how these models can be integrated into physics engines. The proposed friction models are validated on four distinct servo actuators and tested on 2R manipulators, showing significant improvements in accuracy over the standard Coulomb-Viscous model. Our results highlight the importance of considering advanced friction effects in the simulation of servo actuators to enhance the realism and reliability of robotic simulations.

Humanoid robotics faces significant challenges in achieving stable locomotion and recovering from falls in dynamic environments. Traditional methods, such as Model Predictive Control (MPC) and Key Frame Based (KFB) routines, either require extensive fine-tuning or lack real-time adaptability. This paper introduces FRASA, a Deep Reinforcement Learning (DRL) agent that integrates fall recovery and stand up strategies into a unified framework. Leveraging the Cross-Q algorithm, FRASA significantly reduces training time and offers a versatile recovery strategy that adapts to unpredictable disturbances. Comparative tests on Sigmaban humanoid robots demonstrate FRASA superior performance against the KFB method deployed in the RoboCup 2023 by the Rhoban Team, world champion of the KidSize League.

Designing a humanoid locomotion controller is challenging and classically split up in sub-problems. Footstep planning is one of those, where the sequence of footsteps is defined. Even in simpler environments, finding a minimal sequence, or even a feasible sequence, yields a complex optimization problem. In the literature, this problem is usually addressed by search-based algorithms (e.g. variants of A*). However, such approaches are either computationally expensive or rely on hand-crafted tuning of several parameters. In this work, at first, we propose an efficient footstep planning method to navigate in local environments with obstacles, based on state-of-the art Deep Reinforcement Learning (DRL) techniques, with very low computational requirements for on-line inference. Our approach is heuristic-free and relies on a continuous set of actions to generate feasible footsteps. In contrast, other methods necessitate the selection of a relevant discrete set of actions. Second, we propose a forecasting method, allowing to quickly estimate the number of footsteps required to reach different candidates of local targets. This approach relies on inherent computations made by the actor-critic DRL architecture. We demonstrate the validity of our approach with simulation results, and by a deployment on a kid-size humanoid robot during the RoboCup 2023 competition.

Odometry is an important element for the localization of mobile robots. For humanoid robots, it is very prone to integration errors, due to mechanical complexity, uncertainties and foot/ground contacts. Most of the time, a visual odometry is then used to encompass these problems. In this work we propose a method to compensate for odometry drifting using machine learning on a small size low-cost humanoid without vision. This method is tested on different ground conditions and exhibits a significant improvement in odometry accuracy.

This paper reports about the robotic installation set up by the Rhoban Project in the French pavilion of the Expo 2012 of Yeosu, Korea ([6]). The installation has consisted in a humorous show involving humanoid robots and anthropomorphic arms, with the illusion of life as a guideline. We emphasized natural compliant motion and physical interaction in order to make the show attractive. The design raised some issues dealing with robustness of robots, but also with the realism of the motions and the synchronization of the robots with the music.

This paper presents the humanoid robot Acroban. We study two main issues: 1) Compliance and semi-passive dynamics for locomotion of humanoid robots regarding robustness against unknown external perturbations; 2) The advantages of a bio-inspired multi-articulated vertebral column. We combine mechatronic compliance with structural compliance due to the use of flexible materials. And we explore how these capabilities allow to enforce morphological computation in the design of robust dynamic locomotion. We also investigate the use of compliance to design semi-passive motor primitives using the torso and the arms as a system of accumulation/release of potential/kinetic energy.

In this paper, we present a novel approach for the detection and signature extraction for a subclass of polymorphic computer viruses. Our detection scheme offers 0 false negative and a very low false positives detection rate. We use context-free grammars as viral signatures, and design a process able to extract this signature from a single sample of a virus. Signature extraction is achieved through a light manual information gathering process, followed by an automatic static analysis of the binary code of the virus mutation engine.

In this paper we propose a hardware assisted software protection scheme that relies on the use of a resource-limited secure token (e.g. a smart card). The protection consists in externalizing the execution of the sensitive pieces of code of the application to be protected to the token block by block, while the unsensitive code is still executed inside the untrusted computer. We define a generic process: the protection is enforced automatically. Our method relies on static analysis techniques that are used to infer the parts of code to be externalized together with run-time externalization protocol. We have developed a software environment implementing this technology for Java applications.

This video presents the humanoid robot Acroban which is to our knowledge the first humanoid robot which is able to: 1) demonstrate playful, compliant and intuitive physical interaction with children; 2) at the same time move and walk dynamically while keeping its equilibrium even if unpredicted physical interactions are initiated by humans.

Reading and understanding the structure of assembly code is often a tedious and difficult task. It becomes much more difficult when exact timing analysis on control flow paths is required to detect timing attacks. We describe our semi-automated tool VisAA used for visualization of control flow information and timing analysis of execution paths to detect portions of code vulnerable to timing attacks on 8-bit AVR microchip assembly code. Our system provides a great aid by saving much human effort in unravelling and analyzing assembly code.