International audience

We describe a task and motion planning architecture for highly dynamic systems that combines a domain-independent sampling-based deliberative planning algorithm with a global reactive planner. We leverage the recent development of a reactive, vector field planner that provides guarantees of reachability to large regions of the environment even in the face of unknown or unforeseen obstacles. The reachability guarantees can be formalized using contracts that allow a deliberative planner to reason purely in terms of those contracts and synthesize a plan by choosing a sequence of reactive behaviors and their target configurations, without evaluating specific motion plans between targets. This reduces both the search depth at which plans will be found, and the number of samples required to ensure a plan exists, while crucially preserving correctness guarantees. The result is reduced computational cost of synthesizing plans, and increased robustness of generated plans to actuator noise, model misspecification, or unknown obstacles. Simulation studies show that our hierarchical planning and execution architecture can solve complex navigation and rearrangement tasks, even when faced with narrow passageways or incomplete world information.

We propose a novel planning technique for satisfying tasks specified in temporal logic in partially revealed environments. We define high-level actions derived from the environment and the given task itself, and estimate how each action contributes to progress towards completing the task. As the map is revealed, we estimate the cost and probability of success of each action from images and an encoding of that action using a trained neural network. These estimates guide search for the minimum-expected-cost plan within our model. Our learned model is structured to generalize across environments and task specifications without requiring retraining. We demonstrate an improvement in total cost in both simulated and real-world experiments compared to a heuristic-driven baseline.

TheInternet's Domain Name System (DNS) is a part of every web request and e-mail exchange, so DNS failures can be catastrophic, taking out major websites and services. This paper identifies TsuNAME, a vulnerability where some recursive resolvers can greatly amplify queries, potentially resulting in a denial-of-service to DNS services. TsuNAME is caused by cyclical dependencies in DNS records. A recursive resolver repeatedly follows these cycles, coupled with insufficient caching and application-level retries greatly amplify an initial query, stressing authoritative servers. Although issues with cyclic dependencies are not new, the scale of amplification has not previously been understood. We document real-world events in .nz (a country-level domain), where two misconfigured domains resulted in a 50% increase on overall traffic. We reproduce and document root causes of this event through experiments, and demostrate a 500× amplification factor. In response to our disclosure, several DNS software vendors have documented their mitigations, including Google public DNS and Cisco OpenDNS. For operators of authoritative DNS services we have developed and released CycleHunter, an open-source tool that detects cyclic dependencies and prevents attacks. We use CycleHunter to evaluate roughly 184 million domain names in 7 large, top-level domains (TLDs), finding 44 cyclic dependent NS records used by 1.4k domain names. The TsuNAME vulnerability is weaponizable, since an adversary can easily create cycles to attack the infrastructure of a parent domains. Documenting this threat and its solutions is an important step to ensuring it is fully addressed.

Concern has been mounting about Internet centralization over the few last years -- consolidation of traffic/users/infrastructure into the hands of a few market players. We measure DNS and computing centralization by analyzing DNS traffic collected at a DNS root server and two country-code top-level domains (ccTLDs) -- one in Europe and the other in Oceania -- and show evidence of concentration. More than 30% of all queries to both ccTLDs are sent from 5 large cloud providers. We compare the clouds resolver infrastructure and highlight a discrepancy in behavior: some cloud providers heavily employ IPv6, DNSSEC, and DNS over TCP, while others simply use unsecured DNS over UDP over IPv4. We show one positive side to centralization: once a cloud provider deploys a security feature -- such as QNAME minimization -- it quickly benefits a large number of users.

This paper discusses the creation of provably correct control for modular robots from high-level tasks expressed using sentences in structured English. Due to the nature of modular robots, we address problems that include requirements on the geometry and motion characteristics of the robot; these requirements are captured using traits in the specification that are then used in the control generation process. Outlined in this paper is our approach for generating all the lower levels of control for a modular robot given the high-level problem statement. The approach includes the use of a configuration-gait-trait library for characterizing modular robots and tools for populating this library such as a physics-based simulator and gait creator. The approach is demonstrated in simulation and with the CKBot hardware platform.