This paper introduces GET-Zero, a model architecture and training procedure for learning an embodimentaware control policy that can immediately adapt to new hardware changes without retraining. To do so, we present Graph Embodiment Transformer (GET), a transformer model that leverages the embodiment graph connectivity as a learned structural bias in the attention mechanism. We use behavior cloning to distill demonstration data from embodiment-specific expert policies into an embodiment-aware GET model that conditions on the hardware configuration of the robot to make control decisions. We conduct a case study on a dexterous inhand object rotation task using different configurations of a four-fingered robot hand with joints removed and with link length extensions. Using the GET model along with a selfmodeling loss enables GET-Zero to zero-shot generalize to unseen variation in graph structure and link length, yielding a 20 % improvement over baseline methods. All code and qualitative video results are on our project website.

In this work we propose and demonstrate a protocol for a time synchronized channel hopping mesh network for wireless transceivers that use exclusively imprecise and inaccurate on-chip oscillators. This protocol is built on an IEEE 802.15.4 physical layer radio that enables interoperability with protocols such as 6TiSCH or Thread. A calibration-bootstrapped multi-hop mesh network is demonstrated with a single crystal-enabled node acting as the root. The protocol is designed to create a multi-hop mesh while compensating noisy and drifting oscillators and timers. With a 4 s synchronization period, an experimental implementation of the network maintains, in the worst case, 1.8 ms 3σ absolute time synchronization and 820 µs 3σ hop-to-hop synchronization across four hops, under ambient environmental conditions. The resistance to environmental variation is tested by varying one node's supply voltage. With time and frequency feedback from received packets, the node maintains this synchronization with a supply variation of 2.5 mV/s, which is equivalent to a temperature variation of 10°C/min with a packet rate of 0.5 Hz.

Solar-powered actuation of a 15 mN electrostatic MEMS gripper was demonstrated while wirelessly triggered by IEEE 802.15.4 RF signals. The solar-powered gripper was shown to actuate at a rate of 640 um/s. The complete system is composed of three capacitors and three chips: MEMS gripper, microprocessor/crystal-free radio, and solar cell array/high voltage buffer. Control signals for the electrostatic inchworm motors originate from the 3×2×0.3 mm3chip with an ARM Cortex-M0 microprocessor and are passed through 119 V high voltage buffers. Power for all components, including the crystal-free radio, microprocessor, and 119 V buffers, is supplied by a multi-output array of solar cells on a CMOS SOI chip under 200 mW/cm2irradiation.