The growing presence of service robots in human-centric environments, such as warehouses, demands seamless and intuitive human-robot collaboration. In this paper, we propose a collaborative shelf-picking framework that combines multimodal interaction, physics-based reasoning, and task division for enhanced human-robot teamwork.The framework enables the robot to recognize human pointing gestures, interpret verbal cues and voice commands, and communicate through visual and auditory feedback. Moreover, it is powered by a Large Language Model (LLM) that utilizes Chain of Thought (CoT) and a physics-based simulation engine for safely retrieving cluttered stacks of boxes on shelves, a relationship graph for sub-task generation, extraction sequence planning, and decision making. Furthermore, we validate the framework through real-world shelf-picking experiments such as 1) Gesture-Guided Box Extraction, 2) Collaborative Shelf Clearing, and 3) Collaborative Stability Assistance. This work paves the way for more intuitive and effective human-robot collaboration in warehouse environments. A video demonstrating the real-world implementation of our proposed system is available at: https://youtu.be/353zmxMwESg?si=2_T-D7hUZnzl5522

To maximally conserve the critical resource of battery energy, smartphone OSes implement an aggressive system suspend policy that suspends the whole system after a brief period of user inactivity. This burdens developers with the responsibility of keeping the system on, or waking it up, to execute time-sensitive code. Developer mistakes in using the explicit power management unavoidably give rise to energy bugs, which cause significant, unexpected battery drain.

Where is the energy spent inside my app? Despite the immense popularity of smartphones and the fact that energy is the most crucial aspect in smartphone programming, the answer to the above question remains elusive. This paper first presents eprof, the first fine-grained energy profiler for smartphone apps. Compared to profiling the runtime of applications running on conventional computers, profiling energy consumption of applications running on smartphones faces a unique challenge, asynchronous power behavior, where the effect on a component's power state due to a program entity lasts beyond the end of that program entity. We present the design, implementation and evaluation of eprof on two mobile OSes, Android and Windows Mobile.

The WiFi radio in smartphones consumes a significant portion of energy when active. To reduce the energy consumption, the Power Saving Mode was standardized in IEEE 802.11 and two major implementations, Static PSM and Dynamic PSM, have been widely used in mobile devices. Unfortunately, both PSMs have inherent drawbacks: Static PSM is energy efficient but imposes considerable extra delays on data transfers; Dynamic PSM incurs little extra delay but misses energy saving opportunities. In this paper, we first analyze a one-week trace from 10 users and show that more than 80% of all traffic are Web 2.0 flows, which are of very small sizes and short durations. Targeting these short but dominant flows, we propose a system called Percy, to achieve the best of both worlds (Static and Dynamic PSM), i.e., to maximize the energy saving while minimizing the delay of flow completion time. Percy works by deploying a web proxy at the AP and suitably configuring the PSM parameters, and is designed to work with unchanged clients running Dynamic PSM, and unchanged APs and Internet servers. We evaluate our system via trace-driven testbed experiments. Our results show that Percy saves 40-70% energy compared to Dynamic PSM configurations of Nokia, iPhone and Android, while imposing low extra delay that can hardly be perceived by users.

Despite their immense popularity in recent years, smartphones are and will remain severely limited by their battery life. Preserving this critical resource has driven smartphone OSes to undergo a paradigm shift in power management: by default every component, including the CPU, stays off or in an idle state, unless the app explicitly instructs the OS to keep it on! Such a policy encumbers app developers to explicitly juggle power control APIs exported by the OS to keep the components on, during their active use by the app and off otherwise. The resulting power-encumbered programming unavoidably gives rise to a new class of software energy bugs on smartphones called no-sleep bugs, which arise from mis-handling power control APIs by apps or the framework and result in significant and unexpected battery drainage.

Accurate, fine-grained online energy estimation and accounting of mobile devices such as smartphones is of critical importance to understanding and debugging the energy consumption of mobile applications. We observe that state-of-the-art, utilization-based power modeling correlates the (actual) utilization of a hardware component with its power state, and hence is insufficient in capturing several power behavior not directly related to the component utilization in modern smartphones. Such behavior arise due to various low level power optimizations programmed in the device drivers. We propose a new, system-call-based power modeling approach which gracefully encompasses both utilization-based and non-utilization-based power behavior. We present the detailed design of such a power modeling scheme and its implementation on Android and Windows Mobile. Our experimental results using a diverse set of applications confirm that the new model significantly improves the fine-grained as well as whole-application energy consumption accuracy. We further demonstrate fine-grained energy accounting enabled by such a fined-grained power model, via amanually implemented eprof, the energy counterpart of the classic gprof tool, for profiling application energy drain.

This paper argues that a new class of bugs faced by millions of smartphones, energy bugs or ebugs, have become increasingly prominent that already they have led to significant user frustrations. We take a first look at this emerging important technical challenge faced by the smartphones, ebugs, broadly defined as an error in the system (application, OS, hardware, firmware, external conditions or combination) that causes an unexpected amount of high energy consumption by the system as a whole. We first present a taxonomy of the kinds of ebugs based on mining over 39K posts (1.2M before filtering) from 4 online mobile user forum and mobile OS bug repositories. The taxonomy shows the highly diverse nature of smartphone ebugs. We then propose a roadmap towards developing a systematic diagnosing framework for debugging ebugs on smartphones.

While MPLS has been extensively deployed in recent years, little is known about its behavior in practice. We examine the performance of MPLS in Microsoft's online service network (MSN), a well-provisioned multi-continent production network connecting tens of data centers. Using detailed traces collected over a 2-month period, we find that many paths experience significantly inflated latencies. We correlate occurrences of latency inflation with routers, links, and DC-pairs. This analysis sheds light on the causes of latency inflation and suggests several avenues for alleviating the problem.

No abstract available.

The email volume per mailbox has largely remained low and unchanged in the past several decades, and hence mail server performance has largely remained a secondary issue. The steep rise in the amount of unsolicited emails, i.e. spam, in the past decade, however, has permanently disrupted this tranquility of largely steady email volume and turned mail server performance into an increasingly important issue. In this paper, we point out that modern mail servers were not originally designed with email spam in mind, and as such, as the "common case'' workload for mail servers has shifted from legitimate emails to spam emails, we argue it is time to revisit mail server architecture design in following the system design principle of optimizing the common case. In particular, we show how to optimize the performance of three major components of modern mail servers, the concurrency architecture, the disk I/O, and DNSBL lookups, by exploiting the new "common case" workload. An evaluation of our prototype implementation of the enhanced postfix architecture shows that the optimizations significantly reduce the CPU, disk, and network resource consumptions, and improves the throughput of the mail server by 18% under a university departmental mail server workload and by 40% under a spam sinkhole workload.

In bridging the digital divide, two important criteria are cost-effectiveness, and power optimization. While 802.11 is cost-effective and is being used in several installations in the developing world, typical system configurations are not really power efficient. In this paper, we propose a novel "Wake-on-WLAN" mechanism for coarse-grained, on-demand power on/off of the networking equipment at a remote site. The novelty also lies in our implementation of a prototype system using low-power 802.15.4-based sensor motes. We describe the prototype, as well as its evaluation on field in a WiFi testbed. Preliminary estimates indicate that the proposed mechanism can save significant power in typical rural networking settings.