This paper presents a controller for the vertical height of a hopping robot. The ability to accurately change the hop height every step will contribute toward the traversal of discontinuous terrain with limited safe footholds, with application to bipedal or quadrupedal running. A key feature of the approach presented is the use of information from previous hops/steps to inform the control of the current step. As well as avoiding modelling errors, this allows the robot to make on-line adjustments in response to changes in system parameters or the environment. The algorithm is simple enough to be easily implemented on a low power hardware, not requiring computationally demanding optimisation or numerical simulation. The effectiveness of this approach has been demonstrated for constrained vertical hopping in simulation and on a pneumatically actuated hopper.

We present a Bayesian approach to tactile object recognition that improves on state-of-the-art in using single-touch events in two ways. First by improving recognition accuracy from about 90% to about 95%, using about half the number of touches. Second by reducing the number of touches needed for training from about 200 to about 60. In addition, we use a new tactile sensor that is less than one tenth of the cost of widely available sensors. The paper describes the sensor, the likelihood function used with the Naive Bayes classifier, and experiments on a set of ten real objects. We also provide preliminary results to test our approach for its ability to generalise to previously unencountered objects.

The main contribution of this paper is a new step-time controller for a vertically hopping robot leg capable of meeting a demanded step-time instantaneously, meaning within a single hop. The ability to perform hops of an arbitrary and changing size accurately forms the motivation behind the work done here. This would allow control of a running robot's foot placement and thus fast traversal of terrain with limited safe foot placement spots. In this paper, the hopping controller is developed and validated using an articulated, hydraulically actuated leg from the HyQ robot which has been modified to include an elastic foot and constrained to hop vertically. It is shown that instantaneous control over the step-time can be achieved using only joint positions and ground contact senses. This was achieved with a simple feedforward lookup in combination with a proportional and integral action.

This paper introduces a new statistical method for combining vision and robot dynamics to generate trajectories to intercept a moving object. Previous methods only use information from the kinematics without considering the forces needed to move along the trajectory. Using robot dynamics allows extra measures, such as energy efficiency, to be optimised alongside maximising the likelihood of intercepting the target. We derive a statistical model for a vision system and a Lagrangian dynamical model of a robotic arm, showing how to relate joint torques to the vision. The method is tested by applying it to the problem of catching a simulated moving object.

A mathematical formalism is sketched for representing relational structure between agents. n -ary relations, n > 2, require hypernetworks, which generalize binary relation networks. n -ary relations on sets create structure at higher levels of representation to the elements in multilevel systems. The state of a system is represented by its multilevel relational structure. The dynamics of a system are represented by state changes through time. These can be continuous with no change in the hypernetwork topology, but often they are not. Controlling such systems involves taking actions intended to result in desirable state changes. The concept of multilevel hypernetwork can be applied to multiagent systems in general.