Control techniques like MPC can realize contact-rich manipulation which exploits dynamic information, maintaining friction limits and safety constraints. However, contact geometry and dynamics are required to be known. This information is often extracted from CAD, limiting scalability and the ability to handle tasks with varying geometry. To reduce the need for a priori models, we propose a framework for estimating contact models online based on torque and position measurements. To do this, compliant contact models are used, connected in parallel to model multi-point contact and constraints such as a hinge. They are parameterized to be differentiable with respect to all of their parameters (rest position, stiffness, contact location), allowing the coupled robot/environment dynamics to be linearized or efficiently used in gradient-based optimization. These models are then applied for: offline gradient-based parameter fitting, online estimation via an extended Kalman filter, and online gradient-based MPC. The proposed approach is validated on two robots, showing the efficacy of sensorless contact estimation and the effects of online estimation on MPC performance. Video results can be seen at https://youtu.be/CuCTcmn3H-o.

Soft robotic fingers can safely grasp fragile or variable form objects, but their force capacity is limited, especially with less contact area: precision grasps and when objects are smaller or not spherical. Although most research focuses on improving force capacity through mechanical design modifications, optimizing grasping parameters for precision tasks remains crucial. To address this problem, this paper proposes an analytical rotational stability model for soft fingers’ precision grasping, considering grip failure involving slip and dynamic rotational stability. Comprehensive experiments across various objects, grip condition and types of fingers (PneuNet and commercial fingers) are conducted by examining the relationship between grasp parameters and model variables including coulomb friction, bulk stiffness, and dynamic stability. The findings demonstrate the model’s utility in identifying optimal grip parameters that enhance the force capacity of soft fingers without causing dynamic instability. This research contributes to the development of more effective and stable soft robotic fingers for precision grasping tasks.

Planning for contact-rich manipulation involves discontinuous dynamics, which presents challenges to planning methods. Sampling-based planners have higher sample complexity in high-dimensional problems and cannot efficiently handle state constraints such as force limits. Gradient-based solvers can suffer from local optima and their convergence rate is often worse on non-smooth problems. We propose a planning method that is both sampling- and gradient-based, using the Cross-entropy Method to initialize a gradient-based solver, providing better initialization to the gradient-based method and allowing explicit handling of state constraints. The sampling-based planner also allows direct integration of a particle filter, which is here used for online contact mode estimation. The approach is shown to improve performance in MuJoCo environments and the effects of problem stiffness and planing horizon are investigated. The estimator and planner are then applied to an impedance-controlled robot, showing a reduction in solve time in contact transitions to only gradient-based.

Industrial robots have increased payload, repeatability, and reach compared to collaborative robots, however, they have a fixed position controller and low intrinsic admittance. This makes realizing safe contact challenging due to large contact force overshoots in contact transitions and contact instability when the environment and robot dynamics are coupled. To improve safe contact on industrial robots, we propose an admittance controller with inverse model compensation, designed and implemented outside the position controller. By including both the inner loop and outer loop dynamics in its design, the proposed method achieves expanded admittance in terms of increasing both gain and cutoff frequency of the desired admittance. Results from theoretical analyses and experiments on a commercial industrial robot show that the proposed method improves rendering of the desired admittance while maintaining contact stability. We further validate this by conducting actual assembly tasks of plug insertion with fine positioning, switch insertion onto the rail, and colliding the robot end effector with random objects and surfaces, as seen at https://youtu.be/8XfkdHEdWDs.

Admittance and impedance controllers are often purely feedforward, using measured external force or motion, respectively, to generate a reference for an inner-loop controller. In this case, the range of dynamics which can be rendered is limited by the inner-loop, which causes, e.g. contact stability issues for low admittance industrial robots in stiff contact. When both position and force are measured, feedback control can be added to more flexibly reshape the rendered dynamics. This paper uses velocity feedback to increase the admittance of motion-controlled industrial robots in force control applications. This allows an industrial robot with a lower intrinsic admittance, which may be needed for payload, speed, or accuracy, to realize a higher admittance by control, allowing lighter manual guidance and safer contact. This is achieved by a modified disturbance observer, where an inverse dynamic model estimates external forces and amplifies them with positive feedback. This approach is compared with using positive velocity feedback with a shaping filter. Here, velocity reference calculated by the virtual admittance model is modified by the DOB (Dist-Add) or the positive velocity feedback (Vel-Add). When combined with an outer-loop admittance controller, these methods can render a higher admittance while maintaining contact stability compared to standard feedforward admittance control.

Physical human-robot interaction can improve human ergonomics, task efficiency, and the flexibility of automation, but often requires application-specific methods to detect human state and determine robot response. At the same time, many potential human-robot interaction tasks involve discrete modes, such as phases of a task or multiple possible goals, where each mode has a distinct objective and human behavior. In this paper, we propose a novel method for multi-modal physical human-robot interaction that builds a Gaussian process model for human force in each mode of a collaborative task. These models are then used for Bayesian inference of the mode, and to determine robot reactions through model predictive control. This approach enables optimization of robot trajectory based on the belief of human intent, while considering robot impedance and human joint configuration, according to ergonomic- and/or task-related objectives. The proposed method reduces programming time and complexity, requiring only a low number of demonstrations (here, three per mode) and a mode-specific objective function to commission a flexible online human-robot collaboration task. We validate the method with experiments on an admittance-controlled robot, performing a collaborative assembly task with two modes where assistance is provided in full six degrees of freedom. It is shown that the developed algorithm robustly re-plans to changes in intent or robot initial position, achieving online control at 15 Hz.

Safe physical interaction using admittance control on an industrial robot with inner-loop motion control remains challenging. This is partly due to the low intrinsic admittance and stability issues from inner-loop motion control limitations (e.g. bandwidth). To increase the admittance at an interaction point with the user/environment, this paper proposes a robust admittance control architecture. A disturbance observer (DOB) is used to improve effective inner-loop motion control, suppressing the effects of velocity disturbances. The DOB uses the robot's closed-loop task space velocity control as the nominal model, compensating disturbances between the commanded robot velocity and realized robot velocity output. An admittance controller uses measured force to generate robot velocity commands. Detailed analyses are carried out to theoretically evaluate the proposed control system. Experiments conducted on a COMAU RACER-7-1.4 industrial robot verify the effectiveness of the proposed admittance control scheme and stability in environmental contact. Moreover, the proposed method is simple to implement on the existing robot system.

A robot’s dynamics – especially the degree and location of compliance – can significantly affect performance and control complexity. Passive dynamics can be designed with good regions of attraction or limit cycles for a specific task, but achieving flexibility on a range of tasks requires co-design of control. This paper takes an information perspective: the robot dynamics should reduce the amount of information required for a controller to achieve a threshold of performance in a range of tasks. Towards this goal, an iterative method is proposed to minimize the directed information from state to control on discrete-time nonlinear systems. iLQG is used to find a controller and value of information, then the design parameters of the dynamics (e.g. stiffness of end-effector or joint) are optimized to reduce directed information while maintaining a minimum bound on performance. The approach is validated in simulation, on a two-mass system in contact with an uncertain wall position and a high-DOF door opening task, and shown to improve noise robustness and reduce time variance of control gains.

In many series-elastic actuator applications, the ability to safely render a wide range of impedance is important. Advanced torque control techniques such as the disturbance observer (DOB) can improve torque tracking performance, but their impact on safe impedance range is not established. Here, safety is defined with load port passivity, and passivity conditions are developed for two variants of DOB torque control. These conditions are used to determine the maximum safe stiffness and Z-region of the DOB controllers, which are analyzed and compared with the no DOB case. A feedforward controller is proposed which increases the maximum safe stiffness of the DOB approaches. The results are experimentally validated by manual excitation and in a high-stiffness environment.

A robot making contact with an environment or human presents potential safety risks, including excessive collision force. While experiments on the effect of robot inertia, relative velocity, and interface stiffness on collision are in literature, analytical models for maximum collision force are limited to a simplified mass-spring robot model. This simplified model limits the analysis of control (force/torque, impedance, or admittance) or compliant robots (joint and end-effector compliance). Here, the Sobolev norm is adapted to be a system norm, giving rigorous bounds on the maximum force on a stiffness element in a general dynamic system, allowing the study of collision with more accurate models and feedback control. The Sobolev norm can be found through the H2norm of a transformed system, allowing efficient computation, connection with existing control theory, and controller synthesis to minimize collision force. The Sobolev norm is validated, first experimentally with an admittance-controlled robot, then in simulation with a linear flexible-joint robot. It is then used to investigate the impact of control, joint flexibility and end-effector compliance on collision, and a trade-off between collision performance and environmental estimation uncertainty is shown.

Interactive robots offer improved performance in tasks with environmental uncertainty, but accommodating environment input weakens predictions of contact force or position trajectories, making the identification of subtask completion or faults difficult. This paper develops a task monitoring approach for complex assembly tasks that involve transitions between discrete environment dynamic modes. In semi-structured environments, these dynamic modes and their transitions are approximately known a priori, allowing task monitoring through estimation of the current mode and fault detection as a deviation from expected, desired dynamic mode transitions. This allows a more natural description of many interactive tasks, improving robustness to variations in force or position trajectories that impedance control seeks to address. The ability of impedance and admittance controlled robots to identify their environment is investigated, making consideration of joint and end-effector physical compliance. Prior information on environment dynamics and mode transitions allow recursive estimates of dynamic mode suitable for online use, under both full state knowledge and only force/position measurements. Experiments with an admittance controlled robot in a gear assembly task validate the approach.

Human dynamic models are useful in design of physical human-robot and human-robot-environment interaction: informing choice of robot impedance, motivating relaxations to passivity-based safety constraints, and allowing online inference to user intent. Designing for performance objectives such as stable well-damped contact transitions also requires nominal models, but the use of human models in controller design is limited. Established approaches to identify human dynamics apply position or force perturbation and measure the corresponding response, mostly to validate neuromuscular hypotheses on motor control, which raises questions about their transferability to human-led collaboration. Here, human dynamics are identified in a task which closely resembles the final application, where the human leads the robot into contact with a (virtual) wall. This paper investigates the impact of human dynamics on coupled system behavior, and establishes a general framework for identification in human-led scenarios, making consideration of unmeasured human input. Experiments with different stiffness environments allow inference to human dynamics, and characterize the range of human dynamics which can be modulated by the user.

Impedance control offers a theoretical basis for safe interaction between a robot and the environment, but model uncertainty, disturbances and actuation dynamics can compromise the accuracy of the rendered impedance in implementation. If both the interactive force and motion are directly sensed, the relationship between them can be robustly regulated to present the desired impedance dynamics. In this paper, a Disturbance Observer based controller architecture is presented which offers performance robustness for impedance control. Conditions for stability and passivity are developed, then this controller is analyzed on a series-elastic actuated system. The effect of actuation dynamics on both performance and stability is analyzed, then experimental results are presented.

An exoskeleton design for a rhesus macaque subject is motivated, presented, and analyzed. As kinematic properties of the macaque's upper-limb have not been thoroughly studied, this paper introduces methods to determine properties relevant to exoskeleton design. Alignment with biological joints is critical for exoskeleton performance, but there are no accepted kinematic joint models for rhesus macaques. An algorithm is introduced which uses motion capture data to determine an appropriate model for the shoulder complex. An exoskeleton which incorporates this model is introduced, then analyzed. As joint speeds of macaques are also not well studied, a proposed analysis finds an upper bound on the joint speeds required to realize a given end effector speed in an arbitrary direction for all configurations within the workspace.