  This paper presents an overview and comparative analysis of our systems
designed for the following two tracks in SAPIEN ManiSkill Challenge 2021:
  No Interaction Track: The No Interaction track targets for learning policies
from pre-collected demonstration trajectories. We investigate both imitation
learning-based approach, i.e., imitating the observed behavior using classical
supervised learning techniques, and offline reinforcement learning-based
approaches, for this track. Moreover, the geometry and texture structures of
objects and robotic arms are exploited via Transformer-based networks to
facilitate imitation learning.
  No Restriction Track: In this track, we design a Heuristic Rule-based Method
(HRM) to trigger high-quality object manipulation by decomposing the task into
a series of sub-tasks. For each sub-task, the simple rule-based controlling
strategies are adopted to predict actions that can be applied to robotic arms.
  To ease the implementations of our systems, all the source codes and
pre-trained models are available at
\url{https://github.com/caiqi/Silver-Bullet-3D/}.
