For effective operation in challenging outdoor environments, mobile unmanned robots face stiff and competing demands including payload capacity, driving speed, range, as well as the ability to traverse rough terrain. To address these issues we introduce the hybrid wheel-leg quadrupedal robot WaLTER. WaLTER utilizes a unique combination of continuously rotating distal leg joints, actuated wheels, and a roll body DOF to efficiently drive on flat ground and effectively tumble over stairs and difficult, broken terrain. We developed an intuitive teleoperation scheme and employed deep reinforcement learning as proof of concept control techniques for the novel morphology. To test its capabilities, we constructed a multi-body simulation in MuJoCo and a 2.1 kg physical prototype for experimentation on traversability and energy economy. Our testing demonstrated the ability to traverse rougher terrain relative to larger-wheeled counterparts and reliable stair-climbing while maintaining a 4 km range on a 24.4 Wh battery (COT: 1.21).

The human world is full of risks that threaten failure of robotic tasks. Dynamic robots, such as agile drones and walking bipeds, are particularly susceptible to failure because their time to make critical decisions is short. This work seeks a control algorithm which adapts to failures and reprioritizes robot behavior automatically, all at real-time speeds. Our failure-adaptive control framework learns failure probabilities from in situ experience and minimizes the risk of future failures using fast online planners (i.e. model predictive control). By reasoning about probabilities of failure, more imminent risks are automatically prioritized by the framework without manually tuning weighting factors. Further, our low-order probability model is learned using fast convex optimizations, allowing for immediate learning from triggered failures during operation. We demonstrate the framework's capability to learn and plan in real time (< 20 ms) in highly dynamic scenarios with micro-aerial vehicles (i.e. drones). We conduct two experiments: a chase-avoid task, and a chase-avoid - track task. In both scenarios, a single failure causes a categorical shift in robot behavior and the drone will adapt, plan, and execute a non-failing strategy within one second post-failure.

We want robots to complete assigned tasks even when unexpected task pressures arise, either from the robot or the environment. This paper presents a method of both learning sources of task failure in situ and rapidly planning new motions on-the-fly to accommodate them. This “risk-adaptive” approach to robot control uses a few encounters with a novel failure mode to generate a probabilistic failure model which we use to optimize a risk-mitigating motion plan. We demonstrate two toy problems, where risk-adaptive double-integrator agents are introduced to separate environments, each with their own tasks and modes of failure. The agents are not aware a priori of any risks the environments might present, but after one failure, the agents quickly adapt their motion plans and ensure task completion. We further conduct numerical experiments to characterize the algorithm's speed of adaptation with respect to environmental uncertainty. We see this framework as a natural extension for the myriad of robotic applications using model-based motion planners.

Robots encounter many risks that threaten the success of practical locomotion tasks. Legs break, electrical components overheat, and feet can unexpectedly slip. When all risks cannot be completely avoided, how does a robot decide its best action? We present a method for planning robot motions by reasoning about risk-of-failure probabilities instead of applying cost-penalty functions or inflexible path constraints. This work develops a risk-constrained formulation that can be straightforwardly included in existing motion planning optimizations. The risk constraints scale tractably with many risk sources, and in some cases, only add linear constraints to the optimization problem and are therefore compatible with model-predictive control techniques. We present a toy "Puck World" proof-of-concept example and a practical implementation on a planar monopod robot that runs at 3.2 m/s when permitted to take high-risk maneuvers. We believe this risk approach can be used to optimize robot behaviors under numerous conflicting task pressures and model risk-conscious behaviors in animals.