Search

Operating effectively in complex environments while complying with specified constraints is crucial for the safe and successful deployment of robots that interact with and operate around people. In this work, we focus on generating long-horizon trajectories that adhere to novel static and temporally-extended constraints/instructions at test time. We propose a data-driven diffusion-based framework, LTLDoG, that modifies the inference steps of the reverse process given an instruction specified using finite linear temporal logic ($\text{LTL}_f$). LTLDoG leverages a satisfaction value function on $\text{LTL}_f$ and guides the sampling steps using its gradient field. This value function can also be trained to generalize to new instructions not observed during training, enabling flexible test-time adaptability. Experiments in robot navigation and manipulation illustrate that the method is able to generate trajectories that satisfy formulae that specify obstacle avoidance and visitation sequences.

Reliable Human Orientation Estimation (HOE) from a monocular image is critical for autonomous agents to understand human intention. Significant progress has been made in HOE under full observation. However, the existing methods easily make a wrong prediction under partial observation and give it an unexpectedly high confidence. To solve the above problems, this study first develops a method called Part-HOE that estimates orientation from the visible joints of a target person so that it is able to handle partial observation. Subsequently, we introduce a confidence-aware orientation estimation method, enabling more accurate orientation estimation and reasonable confidence estimation under partial observation. The effectiveness of our method is validated on both public and custom-built datasets, and it shows great accuracy and reliability improvement in partial observation scenarios. In particular, we show in real experiments that our method can benefit the robustness and consistency of the Robot Person Following (RPF) task., Comment: Accepted by IROS 2024

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments.

Increasing investment in computing technologies and the advancements in silicon technology has fueled rapid growth in advanced driver assistance systems (ADAS) and corresponding SoC developments. An ADAS SoC represents a heterogeneous architecture that consists of CPUs, GPUs and artificial intelligence (AI) accelerators. In order to guarantee its safety and reliability, it must process massive amount of raw data collected from multiple redundant sources such as high-definition video cameras, Radars, and Lidars to recognize objects correctly and to make the right decisions promptly. A domain specific memory architecture is essential to achieve the above goals. We present a shared memory architecture that enables high data throughput among multiple parallel accesses native to the ADAS applications. It also provides deterministic access latency with proper isolation under the stringent real-time QoS constraints. A prototype is built and analyzed. The results validate that the proposed architecture provides close to 100\% throughput for both read and write accesses generated simultaneously by many accessing masters with full injection rate. It can also provide consistent QoS to the domain specific payloads while enabling the scalability and modularity of the design.

Autonomous mobile manipulation robots that can collect trolleys are widely used to liberate human resources and fight epidemics. Most prior robotic trolley collection solutions only detect trolleys with 2D poses or are merely based on specific marks and lack the formal design of planning algorithms. In this paper, we present a novel mobile manipulation system with applications in luggage trolley collection. The proposed system integrates a compact hardware design and a progressive perception and planning framework, enabling the system to efficiently and robustly collect trolleys in dynamic and complex environments. For the perception, we first develop a 3D trolley detection method that combines object detection and keypoint estimation. Then, a docking process in a short distance is achieved with an accurate point cloud plane detection method and a novel manipulator design. On the planning side, we formulate the robot's motion planning under a nonlinear model predictive control framework with control barrier functions to improve obstacle avoidance capabilities while maintaining the target in the sensors' field of view at close distances. We demonstrate our design and framework by deploying the system on actual trolley collection tasks, and their effectiveness and robustness are experimentally validated., Comment: Accepted to the 2022 International Conference on Robotics and Automation (ICRA 2022)

Wireless cellular System on Chip (SoC) are experiencing unprecedented demands on data rate, latency use case variety. 5G wireless technologies require a massive number of antennas and complex signal processing to improve bandwidth and spectral efficiency. The Internet of Things is causing a proliferation in the number of connected devices, and service categories, such as ultra-reliable low latency, which will produce new use cases, such as self-driving cars, robotic factories, and remote surgery. In addressing these challenges, we can no longer rely on faster cores, or even more silicon. Modem software development is becoming increasingly error prone and difficult as the complexity of the applications and the architectures increase. In this report we propose a Wireless Domain Specific Solution that takes a Dataflow acceleration approach and addresses the need of the SoC to support dataflows that change with use case and user activity, while maintaining the Firm Real Time High Availability with low probability of Heisenbugs that is required in cellular modems. We do this by developing a Domain Specific Architecture that describes the requirements in a suitably abstracted dataflow Domain Specific language. A toolchain is described that automates translation of those requirements in an efficient and robust manner and provides formal guarantees against Heisenbugs. The dataflow native DSA supports the toolchain output with specialized processing, data management and control features with high performance and low power, and recovers rapidly from dropped dataflows while continuing to achieve the real time requirements. This report focuses on the dataflow acceleration in the DSA and the part of the automated toolchain that formally checks the performance and correctness of software running on this dataflow hardware. Results are presented and a summary of future work is given., Comment: 49 pages

Manycore SoC architectures based on on-chip shared memory are preferred for flexible and programmable solutions in many application domains. However, the development of many ported memory is becoming increasingly challenging as we approach the end of Moore's Law while systems requirements demand larger shared memory and more access ports. Memory can no longer be designed simply to minimize single transaction access time, but must take into account the functionality on the SoC. In this paper we examine a common large memory usage in SoC, where the memory is used as storage for large buffers that are then moved for time scheduled processing. We merge two aspects of many ported memory design, combinatorial analysis of interconnect, and geometric analysis of critical paths, extending both to show that in this case the SoC performance benefits significantly from a hierarchical, distributed and staged architecture with lower-radix switches and fractal randomization of memory bank addressing, along with judicious and geometry aware application of speed up. The results presented show the new architecture supports 20% higher throughput with 20% lower latency and 30% less interconnection area at approximately the same power consumption. We demonstrate the flexibility and scalability of this architecture on silicon from a physical design perspective by taking the design through layout. The architecture enables a much easier implementation flow that works well with physically irregular port access and memory dominant layout, which is a common issue in real designs.