Abstract: In a method for recycling energy from stairs, a step platform is moveable between an upper and lower position. An energy storage device, coupled to the platform, stores energy when a downward force is applied thereto, causing the step platform to move to the lower position. The energy storage device (also releases stored energy as the step platform moves from to the upper position. A controllable locking mechanism (locks the step platform in the lower position when the downward force has caused the step platform to move into the lower position. A sensor determines when a downward force has been applied to the next higher step platform. A controller signals the controllable locking mechanism to unlock the step platform when the step platform is in the lower position, and when the downward force has been applied to the next higher step platform.

Abstract: A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.

Abstract: Techniques are disclosed that enable training a plurality of policy networks, each policy network corresponding to a disparate robotic training task, using a mobile robot in a real world workspace. Various implementations include selecting a training task based on comparing a pose of the mobile robot to at least one parameter of a real world training workspace. For example, the training task can be selected based on the position of a landmark, within the workspace, relative to the pose. For instance, the training task can be selected such that the selected training task moves the mobile robot towards the landmark.

Abstract: In a method for recycling energy from stairs, a step platform is moveable between an upper and lower position. An energy storage device, coupled to the platform, stores energy when a downward force is applied thereto, causing the step platform to move to the lower position. The energy storage device (also releases stored energy as the step platform moves from to the upper position. A controllable locking mechanism (locks the step platform in the lower position when the downward force has caused the step platform to move into the lower position. A sensor determines when a downward force has been applied to the next higher step platform. A controller signals the controllable locking mechanism to unlock the step platform when the step platform is in the lower position, and when the downward force has been applied to the next higher step platform.

Abstract: A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.

Abstract: In a mechanism for recycling energy from stairs (200), a step platform (210) is moveable between an upper and lower position. An energy storage device (220), coupled to the platform (210), stores energy when a downward force is applied thereto, causing the step platform (210) to move to the lower position. The energy storage device (220) also releases stored energy as the step platform (210) moves from to the upper position. A controllable locking mechanism (240) locks the step platform (210) in the lower position when the downward force has caused the step platform (210) to move into the lower position. A sensor (250) determines when a downward force has been applied to the next higher step platform (250). A controller (300) signals the controllable locking mechanism (242) to unlock the step platform (250) when the step platform (250) is in the lower position and when the downward force has been applied to the next higher step platform (250).

Abstract: In a mechanism for recycling energy from stairs (200), a step platform (210) is moveable between an upper and lower position. An energy storage device (220), coupled to the platform (210), stores energy when a downward force is applied thereto, causing the step platform (210) to move to the lower position. The energy storage device (220) also releases stored energy as the step platform (210) moves from to the upper position. A controllable locking mechanism (240) locks the step platform (210) in the lower position when the downward force has caused the step platform (210) to move into the lower position. A sensor (250) determines when a downward force has been applied to the next higher step platform (250). A controller (300) signals the controllable locking mechanism (242) to unlock the step platform (250) when the step platform (250) is in the lower position and when the downward force has been applied to the next higher step platform (250).

Abstract: A robot design system, and associated method, that is particularly well-suited for legged robots (e.g., monopods, bipeds, and quadrupeds). The system implements three stages or modules: (a) a motion optimization module; (b) a morphology optimization module; and (c) a link length optimization module. The motion optimization module outputs motion trajectories of the robot's center of mass (COM) and force effectors. The morphology optimization module uses as input the optimized motion trajectories and a library of modular robot components and outputs an optimized robot morphology, e.g., a parameterized mechanical design in which the number of links in each of the legs and other parameters are optimized. The link length optimization module takes this as input and outputs optimal link lengths for a particular task such that the design of a robot is more efficient. The system solves the problem of automatically designing legged robots for given locomotion tasks by numerical optimization.

Abstract: A robot design system, and associated method, that is particularly well-suited for legged robots (e.g., monopods, bipeds, and quadrupeds). The system implements three stages or modules: (a) a motion optimization module; (b) a morphology optimization module; and (c) a link length optimization module. The motion optimization module outputs motion trajectories of the robot's center of mass (COM) and force effectors. The morphology optimization module uses as input the optimized motion trajectories and a library of modular robot components and outputs an optimized robot morphology, e.g., a parameterized mechanical design in which the number of links in each of the legs and other parameters are optimized. The link length optimization module takes this as input and outputs optimal link lengths for a particular task such that the design of a robot is more efficient. The system solves the problem of automatically designing legged robots for given locomotion tasks by numerical optimization.