Ian Clester | Searcharxiv

arXiv2022

Flexible Instruction-Set Semantics via Type Classes

Instruction sets, from families like x86 and ARM, are at the center of many ambitious formal-methods projects. Many verification, synthesis, programming, and debugging tools rely on formal semantics of instruction sets, but different tools can use semantics in rather different ways. As a result, a central challenge for that community is how semantics should be written and what techniques should be used to connect them to new use cases. The best-known work applying single semantics across quite-different tools relies on domain-specific languages like Sail, where the language and its translation tools are specialized to the realm of instruction sets. We decided to explore a different approach, with semantics written in a carefully chosen subset of Haskell. This style does not depend on any new language translators, relying instead on parameterization of semantics over type-class instances. As a result, a semantics can be a first-class object within a logic, and application of a semantics for a new kind of tool can be a first-class operation in the logic, allowing sharing of theorems across applications. Our case study is for the open RISC-V instruction-set family, and we have used a single core semantics to support testing, interactive proof, and model checking of both software and hardware. We especially highlight an application of a first-class semantics within Coq that can be instantiated in different ways within one proof: simulation between variants where multiplication is implemented in hardware or in the machine code of a particular software trap handler.

cs.LO↗

arXiv2021

Robotic Grasping of Fully-Occluded Objects using RF Perception

We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fully-occluded objects in unknown and unstructured environments. Unlike prior systems that are constrained by the line-of-sight perception of vision and infrared sensors, RF-Grasp employs RF (Radio Frequency) perception to identify and locate target objects through occlusions, and perform efficient exploration and complex manipulation tasks in non-line-of-sight settings. RF-Grasp relies on an eye-in-hand camera and batteryless RFID tags attached to objects of interest. It introduces two main innovations: (1) an RF-visual servoing controller that uses the RFID's location to selectively explore the environment and plan an efficient trajectory toward an occluded target, and (2) an RF-visual deep reinforcement learning network that can learn and execute efficient, complex policies for decluttering and grasping. We implemented and evaluated an end-to-end physical prototype of RF-Grasp. We demonstrate it improves success rate and efficiency by up to 40-50% over a state-of-the-art baseline. We also demonstrate RF-Grasp in novel tasks such mechanical search of fully-occluded objects behind obstacles, opening up new possibilities for robotic manipulation. Qualitative results (videos) available at rfgrasp.media.mit.edu

cs.RO↗