With the increasing adoption of robots across industries, it is crucial to focus on developing advanced algorithms that enable robots to anticipate, comprehend, and plan their actions effectively in collaboration with humans. We introduce the Robot Autonomous Motion (RoAM) video dataset, which is collected with a custom-made turtlebot3 Burger robot in a variety of indoor environments recording various human motions from the robot’s ego-vision. The dataset also includes synchronized records of the LiDAR scan and all control actions taken by the robot as it navigates around static and moving human agents. The unique dataset provides an opportunity to develop and benchmark new visual prediction frameworks that can predict future image frames based on the action taken by the recording agent in partially observable scenarios or cases where the imaging sensor is mounted on a moving platform. We have benchmarked the dataset on our novel deep visual prediction framework called ACPNet where the approximated future image frames are also conditioned on action taken by the robot and demonstrated its potential for incorporating robot dynamics into the video prediction paradigm for mobile robotics and autonomous navigation research.

In this work we devise an efficient algorithm that computes the liveness information of program variables. The algorithm employs SSA form and DJ-graphs as representation to build Merge sets. The Merge set of node n , M ( n ) is based on the structure of the Control Flow Graph (CFG) and consists of all nodes where a ϕ-function needs to be placed, if a definition of a variable appears in n . The merge sets of a CFG can be computed using DJ-graphs without prior knowledge of how the variables are used and defined. Later, we can answer the liveness query (as a part of other optimization or analysis phase) by utilizing the knowledge of the use/def of variables, the dominator tree and the pre-computed merge sets. On average, merge sets have been shown to be of size comparable to the Dominance Frontier(DF) set of a CFG and can be computed efficiently for all kinds of applications consisting of both reducible and irreducible loops. This is an advantage over existing algorithms which require additional complexities while handling applications using irreducible loops. For cases where the merge sets have already been created during the SSA construction step, the cost of our algorithm reduces even further when we use these merge sets for liveness computation. We have compared our new algorithm with a recent algorithm for computing liveness based on SSA form, and show how it performs better in practice, though being simpler to understand and implement.

We present a new and practical method of computing ϕ-function for all variables in a function for Static Single Assignment (SSA) form. The new algorithm is based on computing the Merge set of each node in the control flow graph of a function (a node here represents a basic block and the terms will be used interchangeably). Merge set of a node n is the set of nodes N , where ϕ-functions may need to be placed if variables are defined in n . It is not necessary for n to have a definition of a variable in it. Thus, the merge set of n is dictated by the underlying structure of the CFG. The new method presented here precomputes the merge set of every node in the CFG using an iterative approach. Later, these merge sets are used to carry out the actual ϕ-function placement. The advantages are in examples where dense definitions of variables are present (i.e., original definitions of variables---user defined or otherwise, in a majority of basic blocks). Our experience with SSA in the High Level Optimizer (optimization levels +O3/+O4) shows that most examples from the Spec2000 benchmark suite require a high percentage of basic blocks to have their ϕ points computed. Previous methods of computing the same relied on the dominance frontier ( DF ) concept, first introduced by Cytron et al. The method presented in this paper gives a new effective iterative solution to the problem. Also, in cases, where the control flow graph does not change, our method does not require any additional computation for new definitions introduced as part of optimizations. We present implementation details with results from Spec2000 benchmarks. Our algorithm runs faster than the existing methods used.