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Conference paperFrazelle C, Walker I, AlAttar A, et al., 2021,
Continuum robots have strong potential for application in Space environments. However, their modeling is challenging in comparison with traditional rigid-link robots. The Kinematic-Model-Free (KMF) robot control method has been shown to be extremely effective in permitting a rigid-link robot to learn approximations of local kinematics and dynamics (“kinodynamics”) at various points in the robot's task space. These approximations enable the robot to follow various trajectories and even adapt to changes in the robot's kinematic structure. In this paper, we present the adaptation of the KMF method to a three-section, nine degrees-of-freedom continuum manipulator for both planar and spatial task spaces. Using only an external 3D camera, we show that the KMF method allows the continuum robot to converge to various desired set points in the robot's task space, avoiding the complexities inherent in solving this problem using traditional inverse kinematics. The success of the method shows that a continuum robot can “learn” enough information from an external camera to reach and track desired points and trajectories, without needing knowledge of exact shape or position of the robot. We similarly apply the method in a simulated example of a continuum robot performing an inspection task on board the ISS.
Journal articleAlAttar A, Cursi F, Kormushev P, 2021,
Kinematic-model-free redundancy resolution using multi-point tracking and control for robot manipulation, Applied Sciences, Vol: 11, Pages: 1-15, ISSN: 2076-3417
Abstract: Robots have been predominantly controlled using conventional control methods that require prior knowledge of the robots’ kinematic and dynamic models. These controllers can be challenging to tune and cannot directly adapt to changes in kinematic structure or dynamic properties. On the other hand, model-learning controllers can overcome such challenges.Our recently proposed model-learning orientation controller has shown promising ability to simul6 taneously control a three-degrees-of-freedom robot manipulator’s end-effector pose. However, this controller does not perform optimally with robots of higher degrees-of-freedom nor does it resolve redundancies. The research presented in this paper extends the state-of-the-art kinematic9 model-free controller to perform pose control of hyper-redundant robot manipulators and resolve redundancies by tracking and controlling multiple points along the robot’s serial chain. The results show that with more control points, the controller is able to reach desired poses in fewer steps, yielding an improvement of up to 66%, and capable of achieving complex configurations. The algorithm was validated by running the simulation 100 times and it was found that 82% of the times the robot successfully reached the desired target pose within 150 steps.
Conference paperTavakoli A, Fatemi M, Kormushev P, 2021,
Learning to represent action values as a hypergraph on the action vertices, Vienna, Austria, International Conference on Learning Representations
Action-value estimation is a critical component of many reinforcement learning(RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens ofrepresentation learning, good representations of both state and action can facilitateaction-value estimation. While advances in deep learning have seamlessly drivenprogress in learning state representations, given the specificity of the notion ofagency to RL, little attention has been paid to learning action representations. Weconjecture that leveraging the combinatorial structure of multi-dimensional actionspaces is a key ingredient for learning good representations of action. To test this,we set forth the action hypergraph networks framework—a class of functions forlearning action representations in multi-dimensional discrete action spaces with astructural inductive bias. Using this framework we realise an agent class basedon a combination with deep Q-networks, which we dub hypergraph Q-networks.We show the effectiveness of our approach on a myriad of domains: illustrativeprediction problems under minimal confounding effects, Atari 2600 games, anddiscretised physical control benchmarks.
Journal articleRussell F, Takeda Y, Kormushev P, et al., 2021,
Stiffness modulation in walking is critical to maintain static/dynamic stability as well as minimize energy consumption and impact damage. However, optimal, or even functional, stiffness parameterization remains unresolved in legged robotics.We introduce an architecture for stiffness control utilizing a bioinspired robotic limb consisting of a condylar knee joint and leg with antagonistic actuation. The joint replicates elastic ligaments of the human knee providing tuneable compliance for walking. It further locks out at maximum extension, providing stability when standing. Compliance and friction losses between joint surfaces are derived as a function of ligament stiffness and length. Experimental studies validate utility through quantification of: 1) hip perturbation response; 2) payload capacity; and 3) static stiffness of the leg mechanism.Results prove initiation and compliance at lock out can be modulated independently of friction loss by changing ligament elasticity. Furthermore, increasing co-contraction or decreasing joint angle enables increased leg stiffness, which establishes co-contraction is counterbalanced by decreased payload.Findings have direct application in legged robots and transfemoral prosthetic knees, where biorobotic design could reduce energy expense while improving efficiency and stability. Future targeted impact involves increasing power/weight ratios in walking robots and artificial limbs for increased efficiency and precision in walking control.
Journal articleCursi F, Modugno V, Lanari L, et al., 2021,
Bayesian neural network modeling and hierarchical MPC for a tendon-driven surgical robot with uncertainty minimization, IEEE Robotics and Automation Letters, Vol: 6, Pages: 2642-2649, ISSN: 2377-3766
In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.
Journal articleSaputra RP, Rakicevic N, Chappell D, et al., 2021,
In recent years, several robots have been developed and deployed to perform casualty extraction tasks. However, the majority of these robots are overly complex, and require teleoperation via either a skilled operator or a specialised device, and often the operator must be present at the scene to navigate safely around the casualty. Instead, improving the autonomy of such robots can reduce the reliance on expert operators and potentially unstable communication systems, while still extracting the casualty in a safe manner. There are several stages in the casualty extraction procedure, from navigating to the location of the emergency, safely approaching and loading the casualty, to finally navigating back to the medical assistance location. In this paper, we propose a Hierarchical Decomposed-Objective based Model Predictive Control (HiDO-MPC) method for safely approaching and manoeuvring around the casualty. We implement this controller on ResQbot — a proof-of-concept mobile rescue robot we previously developed — capable of safely rescuing an injured person lying on the ground, i.e. performing the casualty extraction procedure. HiDO-MPC achieves the desired casualty extraction behaviour by decomposing the main objective into multiple sub-objectives with a hierarchical structure. At every time step, the controller evaluates this hierarchical decomposed objective and generates the optimal control decision. We have conducted a number of experiments both in simulation and using the real robot to evaluate the proposed method’s performance, and compare it with baseline approaches. The results demonstrate that the proposed control strategy gives significantly better results than baseline approaches in terms of accuracy, robustness, and execution time, when applied to casualty extraction scenarios.
Conference paperCursi F, Modugno V, Kormushev P, 2021,
Model predictive control for a tendon-driven surgical robot with safety constraints in kinematics and dynamics, Las Vegas, USA, International Conference on Intelligence Robots and Systems (IROS), Pages: 7653-7660
In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety andaccuracy of the surgical task. Mechanical designs and actuationschemes have inevitable limitations such as backlash and jointlimits. Moreover, surgical robots need to operate in narrowpathways, which may give rise to additional environmentalconstraints. Therefore, the control strategies must be capableof satisfying the desired motion trajectories and the imposedconstraints. Model Predictive Control (MPC) has proven effective for this purpose, allowing to solve an optimal problem bytaking into consideration the evolution of the system states, costfunction, and constraints over time. The high nonlinearities intendon-driven systems, adopted in many surgical robots, are difficult to be modelled analytically. In this work, we use a modellearning approach for the dynamics of tendon-driven robots.The dynamic model is then employed to impose constraintson the torques of the robot under consideration and solve anoptimal constrained control problem for trajectory trackingby using MPC. To assess the capabilities of the proposedframework, both simulated and real world experiments havebeen conducted
Conference paperRakicevic N, Cully A, Kormushev P, 2020,
Policy manifold search for improving diversity-based neuroevolution, Publisher: arXiv
Diversity-based approaches have recently gained popularity as an alternativeparadigm to performance-based policy search. A popular approach from thisfamily, Quality-Diversity (QD), maintains a collection of high-performingpolicies separated in the diversity-metric space, defined based on policies'rollout behaviours. When policies are parameterised as neural networks, i.e.Neuroevolution, QD tends to not scale well with parameter space dimensionality.Our hypothesis is that there exists a low-dimensional manifold embedded in thepolicy parameter space, containing a high density of diverse and feasiblepolicies. We propose a novel approach to diversity-based policy search viaNeuroevolution, that leverages learned latent representations of the policyparameters which capture the local structure of the data. Our approachiteratively collects policies according to the QD framework, in order to (i)build a collection of diverse policies, (ii) use it to learn a latentrepresentation of the policy parameters, (iii) perform policy search in thelearned latent space. We use the Jacobian of the inverse transformation(i.e.reconstruction function) to guide the search in the latent space. Thisensures that the generated samples remain in the high-density regions of theoriginal space, after reconstruction. We evaluate our contributions on threecontinuous control tasks in simulated environments, and compare todiversity-based baselines. The findings suggest that our approach yields a moreefficient and robust policy search process.
Journal articleRussell F, Kormushev P, Vaidyanathan R, et al., 2020,
Objective: Elucidating the role of structural mechanisms in the knee can improve joint surgeries, rehabilitation, and understanding of biped locomotion. Identification of key features, however, is challenging due to limitations in simulation and in-vivo studies. In particular the coupling of the patello-femoral and tibio-femoral joints with ligaments and its impact on joint mechanics and movement is not understood. We investigate this coupling experimentally through the design and testing of a robotic sagittal plane model. Methods: We constructed a sagittal plane robot comprised of: 1) elastic links representing cruciate ligaments; 2) a bi-condylar joint; 3) a patella; and 4) actuator hamstrings and quadriceps. Stiffness and geometry were derived from anthropometric data. 10° - 110° squatting tests were executed at speeds of 0.1 - 0.25Hz over a range of anterior cruciate ligament (ACL) slack lengths. Results: Increasing ACL length compromised joint stability, yet did not impact quadriceps mechanical advantage and force required for squat. The trend was consistent through varying condyle contact point and ligament force changes. Conclusion: The geometry of the condyles allows the ratio of quadriceps to patella tendon force to compensate for contact point changes imparted by the removal of the ACL. Thus the system maintains a constant mechanical advantage. Significance: The investigation uncovers critical features of human knee biomechanics. Findings contribute to understanding of knee ligament damage, inform procedures for knee surgery and orthopaedic implant design, and support design of trans-femoral prosthetics and walking robots. Results further demonstrate the utility of robotics as a powerful means of studying human joint biomechanics.
Conference paperWang K, Marsh DM, Saputra RP, et al., 2020,
Most state-of-the-art bipedal robots are designedto be highly anthropomorphic and therefore possess legs withknees. Whilst this facilitates more human-like locomotion, thereare implementation issues that make walking with straight ornear-straight legs difficult. Most bipedal robots have to movewith a constant bend in the legs to avoid singularities at theknee joints, and to keep the centre of mass at a constant heightfor control purposes. Furthermore, having a knee on the legincreases the design complexity as well as the weight of the leg,hindering the robot’s performance in agile behaviours such asrunning and jumping.We present SLIDER, an ultra-lightweight, low-cost bipedalwalking robot with a novel knee-less leg design. This nonanthropomorphic straight-legged design reduces the weight ofthe legs significantly whilst keeping the same functionality asanthropomorphic legs. Simulation results show that SLIDER’slow-inertia legs contribute to less vertical motion in the centerof mass (CoM) than anthropomorphic robots during walking,indicating that SLIDER’s model is closer to the widely usedInverted Pendulum (IP) model. Finally, stable walking onflat terrain is demonstrated both in simulation and in thephysical world, and feedback control is implemented to addresschallenges with the physical robot.
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