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This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping. 

Current robotic sensing is mainly visual, which is useful up until the point of contact. To understand how an object is being gripped, tactile feedback is needed. Human grasp is gentle yet firm, with integrated tactile touch feedback. Ras Labs makes Synthetic Muscle™, which is a class of electroactive polymer (EAP) based materials and actuators that sense pressure from gentle touch to high impact, controllably contract and expand at low voltage (battery levels), and attenuate force. The development of this technology towards sensing has provided for fingertip-like sensors that were able to detect very light pressures down to 0.01 N and even 0.005 N, with a wide pressure range to 25 N and more and with high linearity. By using these soft yet robust Tactile Fingertip™ sensors, immediate feedback was generated at the first point of contact. Because these elastomeric pads provided a soft compliant interface, the first point of contact did not apply excessive force, allowing for gentle object handling and control of the force applied to the object. The Tactile Fingertip could also detect a change in pressure location on its surface, i.e., directional glide provided real time feedback, making it possible to detect and prevent slippage by then adjusting the grip strength. Machine learning (ML) and artificial intelligence (AI) were integrated into these sensors for object identification along with the determination of good grip (position, grip force, no slip, no wobble) for pick-and-place and other applications. Synthetic Muscle™ is also being retrofitted as actuators into a human hand-like biomimetic gripper. The combination of EAP shape-morphing and sensing promises the potential for robotic grippers with human hand-like control and tactile sensing. This is expected to advance robotics, whether it is for agriculture, medical surgery, therapeutic or personal care, or in extreme environments where humans cannot enter, including with contagions that have no cure, as well as for collaborative robotics to allow humans and robots to intuitively work safely and effectively together.